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LOS ANGELES — First came a rare solar eclipse, followed by the northern lights, fueled by a solar storm. The next celestial phenomenon will come next month, when skygazers can

Solar eclipses are so last month. Get ready for a 'planetary parade' | Lifestyles | thedailynewsonline.com

The Washington, D.C. region has experienced quite an eventful year for weather and natural phenomena - and there is surely more ahead as we get ready to enter the second half of the year.

From Northern Lights to the Solar Eclipse - here’s DC’s 2024 weather, natural phenomena coverage

Ruby on Rails is an open-source framework based on the Ruby programming language used for developing web applications. Though mostly associated with the backend, the framework is full stack with applications in eCommerce websites and business development. Many giants, such as , , GitHub, etc., use the framework. Then why are we pelted with remarks like ‘Ruby is outdated’ and ‘Rails is irrelevant today'? Netflix Airbnb Rails are going to be 20 years old soon. Though its popularity has declined over the years, it is nowhere near becoming obsolete soon. All opinions suggesting Ruby on Rails is dead are fake rumors. Such rumors are very common in the software development field. Lots of languages and frameworks are pronounced dead, but that is always so far from the truth. So, what’s special about Ruby on Rails, and why should you consider it for your web project in 2023? Brief History of Ruby on Rails Ruby on Rails was developed by David Heinemeier Hansson in August 2004  under an MIT license and was released in 2005. It quickly revolutionized the web development industry with its innovative features, such as seamless database migration, table creation, and scaffolding of views for rapid app development. It also brought a convention-over-configuration paradigm that helps developers at many levels of software design. It is built on the Model-View-Controller framework and works on software development principles such as: : Reduces code and pattern repetitions. Don’t Repeat Yourself : Eases Object-relational mapping. Active Record Pattern : Decreases the number of decisions taken by the developer. Convention-Over-Configuration With these concepts at hand, Ruby on Rails provides quick app development and deployment processes with flexibility and ease of coding, which you may not find in any other similar framework. It helped Ruby on Rails developers to avoid the hassles of coding and focus more on the business and logic aspects of the app. Why Do People Think That Ruby on Rails Is Dying? Ruby on Rails is a powerful framework, especially for developing a prototype application. It is also the most cost-effective. Hence, the reason why businesses flock to it. For developers, it provides a stable, flexible, quick, and simple coding experience. Why the hate, then? For sure, if the technology is not used properly or isn’t updated to the latest versions, it is bound to lose support, and the creators also will not get the crucial feedback required to further develop the framework. Two major issues developers talk about: 1. Performance Issues: Ruby on Rails does have some issues with the CPU. It also has a slower runtime speed compared to other similar frameworks, such as . The issue is noticeable when there is a huge app with lots of traffic and user requests; the framework is bound to take up the load. Node.JS Moreover, the server and database also play an important role in affecting the speed. Otherwise, programs and apps built in Rails are inherently fast. Consider GitHub or Airbnb, which deal with large-scale traffic daily and have their built-in backend Rails. So what’s the issue? Well, Rails has lots of libraries and tools, and inexperienced developers tend to make wrong decisions with them while writing the code. The performance will surely hinder. Otherwise, the creators have actively been working on improving the performance with each update: Ruby 3 aimed to provide thrice the speed of Ruby 2. Rails 6.0 packs in-built features and solutions to simplify web development. Rails 7.0 included more features, such as asynchronous query loading, js bundling-rails integration, etc., to enhance performance. 2. Scalability Issues Ruby on Rails was called ‘difficult to scale up’ when Twitter decided to rewrite its backend in Scala, which was previously in Rails. This was the first instance that started the debate over this issue. But, was Rails the hindrance? Scaling up means handling more clients and users at a given time. To serve them quickly, each server-side element of the application should be configured accordingly to provide optimal performance. A middle-level or small-level application can handle traffic easily. But for large-scale applications, one of the best things to do is to build it in one language so that it becomes easier to connect and sync all the segments together. Twitter moved to Scala because it was written in JavaScript. Additionally, Scala can handle large-scale real-time messaging, so it was a better option for Twitter. But it doesn’t suggest that Ruby on Rails cannot provide scalability. Rails provide a lot of features such as service-oriented architecture, code optimization, horizontal scaling, and threat safety which allows your application to handle more traffic and to easily plug-in additional servers if required. Some large traffic companies that utilize Ruby on Rails are listed in the image below. Ruby on Rails in Comparison With Other Languages Java Java has been the most popular language since its inception. One clear distinction it has with Ruby is in the way of code execution. In Java, code is translated into a virtual machine language before execution. No such translations are required in Ruby before executing the code. Java Ruby On Rails Requires More Lines Of Codes Very Few Lines Of Code Are Required For A Similar Function Slow Development Faster Development Code Needs to be Compiled and Interpreted No Need for a Compiler Testing and Debugging is Difficult Testing, and Debugging is Easier Better Application Performance Better Flexibility And Readability Bigger Community of Experts Not as Big as Java Java Virtual Machine Can Execute Code Faster Since No Compiler, Code Gets Directly Executed Python Python and Ruby are both prominent backend languages with a plethora of libraries and powerful frameworks. They are both at the top of their games and can be utilized for cross-platform applications. However, Ruby offers more freedom in Syntax than Python. Python indulges in simplifying codes by allowing a single statement of code per line. However, this makes every Python code appear similar, hence confusing. Ruby allows flexibility through manifold statements per line. They allow programmers to indent code whenever required. Python Ruby High-Level Language General Purpose Less Object-Oriented Full Object-Oriented Supports Multiple IDEs Only Supports EclipseIDE Has More Libraries Has Fewer Libraries Has Functions Doesn’t Have functions No Modifications On Built-In Classes Can Modify Built-In Classes Multiple Inheritance Single Inheritance. PHP PHP is a general-purpose server-side scripting language like Ruby, which is used to develop almost all types of web content. Like Ruby, it is also fast, pragmatic, flexible, and object-oriented. PHP Ruby On Rails Faster Code Execution Slower Code Execution More Lines Of Codes for a Program Few Lines Of Codes for The Same Program Web Hosting Companies Support PHP Apps RoR Apps Require Unix-Based Servers Testing And Debugging are Difficult Testing And Debugging are Easier Not Much Clear Structure as RoR More Clear Code Structure Requires Low Memory Space Requires High Memory Space Suitable For Any Kind Of Web Application More Suitable For Business Web Applications Perl Perl is a high-level, general-purpose, dynamic programming language. It can render a variety of paradigms, tools, and other language features. It can be used for networking, system administration, etc. One drawback is that programmers tend to work with a single paradigm; thus, the unused features sometimes create confusion. Ruby doesn’t have such issues due to its flexibility and conventions. PERL Ruby on Rails Less Object- Oriented More Object- Oriented Less Number of Libraries More Number of Libraries Supports More Unicode Properties Less Supportive Strings Encoding is Less Explicit Strings Encoding is More Explicit Multiple Variable Types Single Variable Types Supports Auto Conversion of Data Requires Programmers To Convert Types Explicitly Suitable For Any Kind Of Web Application More Suitable For Business Web Applications Why Choose Ruby on Rails Framework For Your Project? Ruby on Rails is a fairly mature technology with a huge community of developers. It is a developer-friendly framework with more than a million web applications and websites built on it and still counting. It provides flexibility, ease of coding, and speed that helps in building a scalable app quickly. Some More Reasons for Choosing Ruby on Rails 1. Business Logic Execution Ruby on Rails can implement complex business logic in a simple and fast process. You can use it with front-end frameworks such as React and have it developed fast. 2. Gems Ruby has a huge collection of gems in the repository. Gems are pre-written lines of codes, libraries, and tools that you can use to write codes more efficiently. They help tremendously in reducing code complexity, provide high-end features, and are reusable. 3. Rapid Development Ruby on Rails is famous for its rapid development process. Creating a project with Ruby is fairly easy, and it works best if you want to deploy your prototype quickly. Ruby utilizes fewer lines of codes and Gems which further helps in the quick development of applications. Compared to other frameworks and languages, Ruby on Rails can develop applications 40-45% more quickly. Conclusion Ruby on Rails had its best time during 2015 and has matured ever since, but its popularity and usefulness are still intact. The framework is highly utilized by startups today due to its advantages in rapid prototyping. The upcoming releases and upgrades also sound promising. With that, we cannot say that RoR has any indications of becoming obsolete anytime soon. It sure has its pros and cons and does lack performance compared to some of the other frameworks, but its advantages far outweigh the drawbacks. Hopefully, shortly we can see Ruby on Rails back at the top of the list again. Also published here.

Why Ruby on Rails Remains an Essential Framework in 2023

JPA was born as the acronym for Java Persistence API. When Java EE was donated to the Eclipse Foundation under the project, the name of the API changed to Jakarta Persistence but the term JPA is still in use. JPA solves the object-relational impedance mismatch by allowing you to map Java objects to database tables and is one of the most (if not the most) used persistence frameworks for Java. Jakarta EE JPA is an API specification that someone can implement–JPA providers or implementations. The most popular JPA implementation is Hibernate ORM. You can use Hibernate ORM without JPA or through JPA. One potential advantage of using it with JPA is that you can move between implementations if you want (something I have never seen happening, though). Another advantage is that someone with experience in, say EclipseLink or Apache OpenJPA, then they can use at least part of that experience when moving to Hibernate. This article gets you started with the Hibernate implementation of JPA. We’ll use only standard features and cover only the basics. If you are looking for a comprehensive course on JPA or Hibernate, I recommend reading the and the official . JPA spec Hibernate docs Here’s a video version of this article, in case you want to see the concepts in action: Adding JPA and Hibernate to a Maven Project Here are the dependencies you need to add to the file: Maven pom.xml <dependency>
    <groupId>jakarta.persistence</groupId>
    <artifactId>jakarta.persistence-api</artifactId>
    <version>3.0.0</version>
</dependency>
<dependency>
    <groupId>org.hibernate</groupId>
    <artifactId>hibernate-core-jakarta</artifactId>
    <version>5.6.4.Final</version>
</dependency>
<dependency>
    <groupId>org.glassfish.jaxb</groupId>
    <artifactId>jaxb-runtime</artifactId>
    <version>3.0.0</version>
</dependency> You also need a driver. For example, if you are using a MariaDB database or SkySQL instance, add the following as well (check the latest version and see in the 3.0 series): JDBC what’s new <dependency>
    <groupId>org.mariadb.jdbc</groupId>
    <artifactId>mariadb-java-client</artifactId>
    <version>3.0.3</version>
</dependency> If you use Gradle, you can use an online Maven to Gradle dependency . converter Creating a Database You can use any database you want, but let’s continue with MariaDB. You can create a MariaDB database in the cloud using SkySql. It's and it doesn’t require credit card details. Create an account and take a look at the for more information on how to get started. Note: free documentation Connect to the database using an SQL client and create the following database: CREATE DATABASE jpa_demo; Defining a Persistence Unit A Persistence Unit is the logical grouping of a set of classes of which objects can be persisted, plus configuration parameters like database connection and pooling options. A Persistence Unit is defined in a file in the directory. Here’s the one we’ll use in this article: persistence.xml src/main/resources/META-INF/ <persistence xmlns="http://java.sun.com/xml/ns/persistence"
             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
             xsi:schemaLocation="http://java.sun.com/xml/ns/persistence http://java.sun.com/xml/ns/persistence/persistence_2_0.xsd"
             version="2.0">
    <persistence-unit name="jpa-demo-local" transaction-type="RESOURCE_LOCAL">
        <properties>
            <property name="jakarta.persistence.jdbc.url" value="jdbc:mariadb://localhost:3306/jpa_demo"/>
            <property name="jakarta.persistence.jdbc.user" value="user"/>
            <property name="jakarta.persistence.jdbc.password" value="password"/>
            <property name="jakarta.persistence.schema-generation.database.action" value="drop-and-create"/>
        </properties>
    </persistence-unit>
</persistence> Pay close attention to the and parameters. Every Persistence Unit needs a name that we can use later in the code. The transaction type indicates who manages the transactions, JPA or the server. In this example we want JPA to manage the transactions, but in more complex scenarios that require distributed transactions across multiple databases or services like JMS and JCA, we would use JTA instead. This is an advanced topic we won’t cover in this article. name transaction-type We added properties to specify the JDBC connection URL and the database user and password. We also activated a database action to drop and create the database schema automatically. This will make JPA drop the database (deleting all the tables and schema) and recreate them any time we start the application. This obviously is not a good idea in production environments, but very useful during development, especially when you are starting to define the database objects through JPA Entities. Implementing an Entity An is a class of which instances we want to persist in the database. You can configure them through Java annotations or XML. Study the following example paying close attention to the annotations: Entity package com.example;
 
import jakarta.persistence.*;
 
import java.util.Objects;
 
@Entity
@Table(name = "programming_language")
public class ProgrammingLanguage {
 
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    @Column(name = "pl_id")
    private Integer id;
 
    @Column(name = "pl_name")
    private String name;
 
    @Column(name = "pl_rating")
    private Integer rating;
 
    public ProgrammingLanguage() {
    }
 
    public ProgrammingLanguage(String name, Integer rating) {
        this.name = name;
        this.rating = rating;
    }
 
    @Override
    public boolean equals(Object o) {
        if (this == o) return true;
        if (o == null || getClass() != o.getClass()) return false;
        ProgrammingLanguage that = (ProgrammingLanguage) o;
        return Objects.equals(id, that.id);
    }
 
    @Override
    public int hashCode() {
        return Objects.hash(id);
    }
 
    public Integer getId() {
        return id;
    }
 
    public void setId(Integer id) {
        this.id = id;
    }
 
    public String getName() {
        return name;
    }
 
    public void setName(String name) {
        this.name = name;
    }
 
    public Integer getRating() {
        return rating;
    }
 
    public void setRating(Integer rating) {
        this.rating = rating;
    }
} At a minimum, a JPA Entity class is marked by , has a field annotated with , a default constructor, and field accessors (getters). In the previous example, we added extra configuration. For example, we configured the names of the SQL table and column names, and a generated value to let the database assign primary keys. We also added a custom constructor (additional to the required non-arg constructor), and methods, and field mutators (setters). @Entity @Id equals(Object) hashCode() The entry point of the JPA API is the class. Frameworks like Spring Framework and JakartaEE might provide instances of this type to your application. If you are not using them, you can create an instance specifying the name Persistence Unit to use: EntityManagerFactory EntityManagerFactory entityManagerFactory =
        Persistence.createEntityManagerFactory("jpa-demo-local"); Ideally, you should have only one instance of for each Persistence Unit per application and create objects (much cheaper to create) as needed. Persistence operations must be inside a transaction boundary: EntityManagerFactory EntityManager EntityManager entityManager = entityManagerFactory.createEntityManager();
EntityTransaction transaction = entityManager.getTransaction();
transaction.begin();
 
... persistence logic here ...
 
transaction.commit(); // or transaction.rollback(); The class includes methods to query and update data. For example, if we want to save a object in the database, we can add the following persistence logic to the previous snippet of code: EntityManager ProgrammingLanguage entityManager.persist(new ProgrammingLanguage("Java", 10)); Or if we want to get all the programming languages stored in the database as a list of objects: ProgrammingLanguage List<ProgrammingLanguage> list = entityManager.createQuery(
        "select p from ProgrammingLanguage p where p.rating > 5",
        ProgrammingLanguage.class
).getResultList(); Notice that the query in the string is not SQL. If you recall from where we implemented the Entity ( ) we used the annotation to configure the name of the table as . The query language is called (JPQL), a platform-independent object-oriented language that is part of the JPA specification. This makes your database queries portable between databases. ProgrammingLanguage @Table programming_language Jakarta Persistence Query Language some of the methods available in the interface to get an idea of all the persistence operations available. This is the interface that you’ll use the most when using JPA directly. Check EntityManager Implementing a Simple JPA Service Class Even though we used the most simple use case and ignored things such as transaction rollback in case of errors or controlled conditions, we need 5 lines of boilerplate code to run a simple query or to save, delete or update an Entity. In a Java SE application like the one we are implementing in this article, it’d be useful to have a utility class to encapsulate all the boilerplate code and make the usage of JPA easier. This utility class should include the logic to create one per application, create a new and begin, close, and commit transactions automatically. PersistenceManagerFactory EntityManager Here’s an example of such a utility class implemented as a singleton that takes care of rolling back a transaction in case of a failure. Take into account that if you are using frameworks like Jakarta EE or Spring Data, you don’t really need this kind of class: package com.example;
 
import jakarta.persistence.EntityManager;
import jakarta.persistence.EntityManagerFactory;
import jakarta.persistence.EntityTransaction;
import jakarta.persistence.Persistence;
 
import java.util.function.Function;
 
public class JPAService {
 
    private static JPAService instance;
    private EntityManagerFactory entityManagerFactory;
 
    private JPAService() {
        entityManagerFactory = Persistence.createEntityManagerFactory("jpa-demo-local");
    }
 
    public static synchronized JPAService getInstance() {
        return instance == null ? instance = new JPAService() : instance;
    }
 
    public void shutdown() {
        if (entityManagerFactory != null) {
            entityManagerFactory.close();
            instance = null;
        }
    }
 
    public <T> T runInTransaction(Function<EntityManager, T> function) {
        EntityManager entityManager = entityManagerFactory.createEntityManager();
        EntityTransaction transaction = entityManager.getTransaction();
        transaction.begin();
        boolean success = false;
        try {
            T returnValue = function.apply(entityManager);
            success = true;
            return returnValue;
 
        } finally {
            if (success) {
                transaction.commit();
            } else {
                transaction.rollback();
            }
        }
    }
} By using this class we can simplify the code required to use an . For example, to run a JQL query we only need to enclose the logic in a call: EntityManager runInTransaction(Function<EntityManager>, T) List<ProgrammingLanguage> programmingLanguages = jpaService.runInTransaction(entityManager ->
        entityManager.createQuery(
                "select p from ProgrammingLanguage p where p.rating > 5",
                ProgrammingLanguage.class
        ).getResultList()); Here’s an example of a full Java application that uses this class to create programming languages with random ratings and show the ones with a rating greater than 5: package com.example;
 
import java.util.Arrays;
import java.util.List;
 
public class Application {
 
    private static final JPAService jpaService = JPAService.getInstance();
 
    public static void main(String[] args) {
        try {
            createProgrammingLanguages();
            printTopProgrammingLanguages();
 
        } finally {
            jpaService.shutdown();
        }
    }
 
    private static void createProgrammingLanguages() {
        jpaService.runInTransaction(entityManager -> {
            Arrays.stream("Java,C++,C#,JavaScript,Rust,Go,Python,PHP".split(","))
                    .map(name -> new ProgrammingLanguage(name, (int) (Math.random() * 10)))
                    .forEach(entityManager::persist);
            return null;
        });
    }
 
    private static void printTopProgrammingLanguages() {
        List<ProgrammingLanguage> programmingLanguages = jpaService.runInTransaction(entityManager ->
                entityManager.createQuery(
                        "select p from ProgrammingLanguage p where p.rating > 5",
                        ProgrammingLanguage.class
                ).getResultList());
 
        programmingLanguages.stream()
                .map(pl -> pl.getName() + ": " + pl.getRating())
                .forEach(System.out::println);
    }
} Useful Resources We merely scratched the surface of JPA and Hibernate in this article. Fortunately, being one of the most popular Java specifications, a ton of useful resources are available online. Also Published Here

How to Get Started With JPA/Hibernate

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 2 OBSERVATIONS 2.1 Photometry The Kepler space telescope (Koch et al. 2010) was launched in March 2009 with the primary aim of detecting Earth-like extrasolar planets around solar-like stars. However, the mission has also provided a huge amount of high-quality data on stars and stellar systems (Hong et al. 2022). The data were collected using two modes of observation (Gilliland et al. 2010), long cadence (29.5 min sampling rate) and short cadence (58.5 s sampling rate). To keep the ht). solar arrays pointed toward the Sun during its Earth-trailing heliocentric orbit, the spacecraft was rotated by 90◦ every three months (one quarter). Primary functions of the Kepler photometric analysis module of the Kepler science data processing pipeline are responsible for generating the Simple Aperture Photometric (SAP) flux time series of the observed objects, while secondary photometric analysis functions performed by the Pre-Search Data Conditioning (PDC) module support systematic error correction, giving rise to the PDCSAP fluxes (see Jenkins 2017, for further details on the SAP and PDCSAP measurements). KIC 9851944 was observed by Kepler in six quarters (0, 12, 13, 16). The light curves from the Kepler quarters and TESS sectors mentioned above were downloaded from the Mikulski Archive for Space Telescopes (MAST) and are used in Section 3 to determine the ephemeris of the system, as well as Section 6 to obtain the final model of the light curve. Both SAP and PDCSAP measurements are available; the SAP and PDCSAP fluxes were similar, which we verified by inspecting the SAP fluxes with the PDCSAP fluxes overplotted after dividing them by their median flux values to put them on the same scale. Thus, we used the SAP measurements to avoid possible biases due to the additional processing applied to the PDCSAP data. A second-order polynomial was fitted to fluxes that correspond to positions of quadrature, i.e., the maximum of the ellipsoidal brightening, to estimate systematic trends present in the light curves. Subtracting the difference between this polynomial and the median flux of the light curve yields a model for the local median level of out-of-eclipse flux. This model was then divided out to remove systematic trends. The residual value of a smoothed version of the light curve subtracted from the observed light curve was calculated and observed fluxes that deviated by more than 1% were rejected. Fluxes were converted to magnitudes and errors were propagated following Prša (2018). The short cadence Kepler light curve from Quarter 0 and the two-minute cadence TESS light curve from Sector 15, after performing these operations, are shown in the top and middle panels of Fig.1, respectively. ely. KIC 9851944 was also observed by the Wide Angle Search for Planets (WASP) telescope (Butters et al. 2010) between May 2007 and July 2010. WASP consisted of two robotic observatories, one in the Nothern Hemisphere at Observatorio del Roque de los Muchachos on La Palma and the other at the South African Astronomical Observatory (SAAO), each with eight 20 mm telescopes on a single mount. Observations of KIC 9851944 collected by WASP between May 2007 and July 2007 as well as between June 2008 and July 2008 are shown in the bottom panels of Fig.1 phase folded about the orbital period determined in Section 3. The data collected by WASP for KIC 9851944 were only used to constrain the times of primary minima when performing preliminary fits to the Kepler and TESS light curves in Section 3, to determine the orbital ephemeris. 2.2 Spectroscopy A set of 33 spectroscopic observations were obtained for the target using the Hamilton échelle spectrograph (Vogt 1987) on the Shane 3 m telescope at Lick Observatory during two observing runs, one in July 2012 and the other in June 2013. The data were obtained using CCD chip no. 4, giving a wavelength coverage of 340 – 900 nm over 89 échelle orders at a resolving power of R ≈ 60000. The data were reduced using the standard pipeline for the spectrograph (e.g. Fischer et al. 2014). Flat fields were obtained with a quartz lamp and divided from the spectra. The wavelength calibration was obtained from exposures of a thorium-argon emission lamp taken roughly every hour during the night. Details for the normalization of the one-dimensional extracted spectra are given in Sections 4 and 5 because each of those components of the analysis used different approaches. Table 1 gives the epochs of each of the 33 spectrosopic observations as well as the signal-to-noise ratio (S/N), estimated as the square root of the counts close to the peak of the best-exposed échelle order. Also given are the corresponding RVs of each component that are derived in Section 4. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 2 OBSERVATIONS 2.1 Photometry The Kepler space telescope (Koch et al. 2010) was launched in March 2009 with the primary aim of detecting Earth-like extrasolar planets around solar-like stars. However, the mission has also provided a huge amount of high-quality data on stars and stellar systems (Hong et al. 2022). The data were collected using two modes of observation (Gilliland et al. 2010), long cadence (29.5 min sampling rate) and short cadence (58.5 s sampling rate). To keep the ht). solar arrays pointed toward the Sun during its Earth-trailing heliocentric orbit, the spacecraft was rotated by 90◦ every three months (one quarter). Primary functions of the Kepler photometric analysis module of the Kepler science data processing pipeline are responsible for generating the Simple Aperture Photometric (SAP) flux time series of the observed objects, while secondary photometric analysis functions performed by the Pre-Search Data Conditioning (PDC) module support systematic error correction, giving rise to the PDCSAP fluxes (see Jenkins 2017, for further details on the SAP and PDCSAP measurements). KIC 9851944 was observed by Kepler in six quarters (0, 12, 13, Kepler 16). The light curves from the Kepler quarters and TESS sectors mentioned above were downloaded from the Mikulski Archive for Space Telescopes (MAST) and are used in Section 3 to determine the ephemeris of the system, as well as Section 6 to obtain the final model of the light curve. Both SAP and PDCSAP measurements are available; the SAP and PDCSAP fluxes were similar, which we verified by inspecting the SAP fluxes with the PDCSAP fluxes overplotted after dividing them by their median flux values to put them on the same scale. Thus, we used the SAP measurements to avoid possible biases due to the additional processing applied to the PDCSAP data. A second-order polynomial was fitted to fluxes that correspond to positions of quadrature, i.e., the maximum of the ellipsoidal brightening, to estimate systematic trends present in the light curves. Subtracting the difference between this polynomial and the median flux of the light curve yields a model for the local median level of out-of-eclipse flux. This model was then divided out to remove systematic trends. The residual value of a smoothed version of the light curve subtracted from the observed light curve was calculated and observed fluxes that deviated by more than 1% were rejected. Fluxes were converted to magnitudes and errors were propagated following Prša (2018). The short cadence Kepler light curve from Quarter 0 and the two-minute cadence TESS light curve from Sector 15, after performing these operations, are shown in the top and middle panels of Fig.1, respectively. ely. KIC 9851944 was also observed by the Wide Angle Search for Planets (WASP) telescope (Butters et al. 2010) between May 2007 and July 2010. WASP consisted of two robotic observatories, one in the Nothern Hemisphere at Observatorio del Roque de los Muchachos on La Palma and the other at the South African Astronomical Observatory (SAAO), each with eight 20 mm telescopes on a single mount. Observations of KIC 9851944 collected by WASP between May 2007 and July 2007 as well as between June 2008 and July 2008 are shown in the bottom panels of Fig.1 phase folded about the orbital period determined in Section 3. The data collected by WASP for KIC 9851944 were only used to constrain the times of primary minima when performing preliminary fits to the Kepler and TESS light curves in Section 3, to determine the orbital ephemeris. 2.2 Spectroscopy A set of 33 spectroscopic observations were obtained for the target using the Hamilton échelle spectrograph (Vogt 1987) on the Shane 3 m telescope at Lick Observatory during two observing runs, one in July 2012 and the other in June 2013. The data were obtained using CCD chip no. 4, giving a wavelength coverage of 340 – 900 nm over 89 échelle orders at a resolving power of R ≈ 60000. The data were reduced using the standard pipeline for the spectrograph (e.g. Fischer et al. 2014). Flat fields were obtained with a quartz lamp and divided from the spectra. The wavelength calibration was obtained from exposures of a thorium-argon emission lamp taken roughly every hour during the night. Details for the normalization of the one-dimensional extracted spectra are given in Sections 4 and 5 because each of those components of the analysis used different approaches. Table 1 gives the epochs of each of the 33 spectrosopic observations as well as the signal-to-noise ratio (S/N), estimated as the square root of the counts close to the peak of the best-exposed échelle order. Also given are the corresponding RVs of each component that are derived in Section 4.

Physical Properties of the Eclipsing Binary KIC 9851944: Observation

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 4 RADIAL VELOCITY ANALYSIS The spectral range between 4400–4800 Å is a suitable region to carry out the RV extraction procedure because there are many wellresolved spectral lines and the region does not contain any wide Balmer lines. Thirteen échelle orders within this range were corrected for cosmic ray spikes by first resampling onto a homogeneous wavelength grid before using a median filter to smooth them, then resampling the orders back to the original grid and subtracting them from the observed spectrum (Blanco-Cuaresma et al. 2014a). Pixels where the residual deviated by a threshold percentage, typically between 5 and 10%, were masked and their values were corrected for using linear interpolation. The appropriate value for the threshold was investigated for each order individually based on visual inspections of resulting flagged pixels. The blaze signature was removed using the method of Xu et al. (2019), where alpha shape fitting (see, Edelsbrunner et al. 1983) combined with local polynomial regression is used to estimate the continuum without dividing out important spectral features. Small errors in the continuum estimation are magnified where the blaze function approaches zero. Following Xu et al. (2019), a better estimation at the edges can be obtained by taking a weighted average in the overlapping regions and applying weights such that pixels further away from the edge within their own order are favoured. For this to be applied correctly, the orders were re-sampled onto grids with a common wavelength step so that the pixel averages were calculated for corresponding wavelength values between neighbouring orders. A useful result from the edge correction procedure is that neighbouring orders share a common edge and can be merged after truncating the orders in the centre of the overlapping regions. The following analysis was carried out after merging the 13 spectral orders with wavelengths between 4400-4800 Å using this process. The pulsating eclipsing binary KIC 9851944 Our own implementation of the TODCOR two-dimensional cross-correlation algorithm (Zucker & Mazeh 1994) was used to extract the RVs. Systematic errors arise when using the cross-correlation technique to extract RVs because neighbouring peaks and sidelobes in the doubled-peaked CCF disturb each other, and this is related to blending between the spectral lines of the two components (Andersen 1975; Latham et al. 1996). The result is that the estimated RVs may be shifted relative to the true value, where the size and direction of the shift depends on the phase (see Fig.4), i.e, the relative Doppler shifts between the components (Latham et al. 1996). This systematic error can be minimised by correcting the RVs for this effect (e.g., Latham et al. 1996; Torres et al. 1997; Torres et al. 2000; Torres & Ribas 2002; Southworth & Clausen 2007). A preliminary orbit was obtained by making an initial fit to the RVs extracted using TODCOR. A synthetic SB2 model was then produced with orbital parameters corresponding to the initial fit by Doppler shifting and adding the template spectra weighted by their relative light contributions as estimated by the resulting value for ℓB/ℓA from the initial TODCOR run. The TODCOR algorithm was then used to extract the known RVs from the synthetic SB2 model at orbital phases corresponding to the observations. The difference between the calculated RV and the known RV of the model gives an estimation for the magnitude and direction of the systematic error at that orbital phase. The correction was then added to the actual RVs measured from the observed spectra. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 4 RADIAL VELOCITY ANALYSIS The spectral range between 4400–4800 Å is a suitable region to carry out the RV extraction procedure because there are many wellresolved spectral lines and the region does not contain any wide Balmer lines. Thirteen échelle orders within this range were corrected for cosmic ray spikes by first resampling onto a homogeneous wavelength grid before using a median filter to smooth them, then resampling the orders back to the original grid and subtracting them from the observed spectrum (Blanco-Cuaresma et al. 2014a). Pixels where the residual deviated by a threshold percentage, typically between 5 and 10%, were masked and their values were corrected for using linear interpolation. The appropriate value for the threshold was investigated for each order individually based on visual inspections of resulting flagged pixels. The blaze signature was removed using the method of Xu et al. (2019), where alpha shape fitting (see, Edelsbrunner et al. 1983) combined with local polynomial regression is used to estimate the continuum without dividing out important spectral features. Small errors in the continuum estimation are magnified where the blaze function approaches zero. Following Xu et al. (2019), a better estimation at the edges can be obtained by taking a weighted average in the overlapping regions and applying weights such that pixels further away from the edge within their own order are favoured. For this to be applied correctly, the orders were re-sampled onto grids with a common wavelength step so that the pixel averages were calculated for corresponding wavelength values between neighbouring orders. A useful result from the edge correction procedure is that neighbouring orders share a common edge and can be merged after truncating the orders in the centre of the overlapping regions. The following analysis was carried out after merging the 13 spectral orders with wavelengths between 4400-4800 Å using this process. The pulsating eclipsing binary KIC 9851944 The pulsating eclipsing binary KIC 9851944 The pulsating eclipsing binary KIC 9851944 Our own implementation of the TODCOR two-dimensional cross-correlation algorithm (Zucker & Mazeh 1994) was used to extract the RVs. Systematic errors arise when using the cross-correlation technique to extract RVs because neighbouring peaks and sidelobes in the doubled-peaked CCF disturb each other, and this is related to blending between the spectral lines of the two components (Andersen 1975; Latham et al. 1996). The result is that the estimated RVs may be shifted relative to the true value, where the size and direction of the shift depends on the phase (see Fig.4), i.e, the relative Doppler shifts between the components (Latham et al. 1996). This systematic error can be minimised by correcting the RVs for this effect (e.g., Latham et al. 1996; Torres et al. 1997; Torres et al. 2000; Torres & Ribas 2002; Southworth & Clausen 2007). A preliminary orbit was obtained by making an initial fit to the RVs extracted using TODCOR. A synthetic SB2 model was then produced with orbital parameters corresponding to the initial fit by Doppler shifting and adding the template spectra weighted by their relative light contributions as estimated by the resulting value for ℓB/ℓA from the initial TODCOR run. The TODCOR algorithm was then used to extract the known RVs from the synthetic SB2 model at orbital phases corresponding to the observations. The difference between the calculated RV and the known RV of the model gives an estimation for the magnitude and direction of the systematic error at that orbital phase. The correction was then added to the actual RVs measured from the observed spectra.

Physical Properties of the Eclipsing Binary KIC 9851944: Radial Velocity Analysis

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 5 SPECTRAL ANALYSIS 5.1 Atmospheric parameters Since we planned to apply the method of SPD to extract the individual spectra of the components, normalisation of the observed spectra was of critical importance. We used a different approach than that in Section 4, where we extracted RVs. Here, we used the dedicated code described in Kolbas et al. (2015). First, the blaze function of échelle orders was fitted with a high-order polynomial function. Then, the normalised échelle orders were merged. When overlapping regions of successive échelle orders are sufficiently long, the very ends were cut off because of their low S/N. Échelle orders containing broad Balmer lines, in which it is not possible to define the blaze function with enough precision, were treated in a special way. For these échelle orders, the blaze function was interpolated from adjacent orders. This produces more reliable normalisation in spectral regions with broad Balmer lines than the usual pipeline procedures. For recent applications of this approach, please see Pavlovski et al. (2018, 2023); Lester et al. (2019, 2022); Wang et al. (2020, 2023). Overall, a slight difference in line depth between the two components is seen. The more massive component has deeper lines and slightly faster rotation. The Balmer lines are broadened by Stark broadening, and generally are not sensitive to the rotational broadening. Thus, we first optimised portions of the disentangled spectra free of the Balmer lines, primarily to discern the vsini values. We then performed optimal fitting of disentangled spectra centred on the Hβ and Hα lines, with fixed vsini. The light ratio from the optimal fitting of the wings of Hβ and Hγ lines, spectral segments containing only metal lines, and the Mg I triplet at around 5180 Å, are 1.315±0.018, 1.304±0.025 and 1.278±0.033 (Table 3). All three values for the light ratio are consistent within their 1σ uncertainties, with the light ratio determined from the wings of Hβ line being the most precise of the three. Guo et al. (2016) determined atmospheric parameters from tomographically reconstructed spectra of the components. Their spectra cover the wavelength range 3930–4610 Å at a medium spectral resolution of R = 6000. The analysis by Guo et al. (2016) was similar to ours, but with an important difference that they fitted complete separated spectra, whilst we concentrate on the wings of Balmer lines. Our results for the Balmer lines corroborate their findings to within 1σ. We also find good agreement for the vsini values, which are also within 1σ for both components. Guo et al. (2016) obtained a mean light ratio 1.34±0.03 from spectra centred at 4275 Å – this is slightly larger than but still consistent with our own results. 5.2 Direct fitting for the light ratio We were initially unable to constrain the light ratio of the system from the photometric analysis alone (see Section 3), so estimated it using several independent methods. The first method was the TODCOR light ratio reported in Table 2, the second was the optimal fitting of the disentangled spectra (ODS), and a third method is developed here. We repeated the process for all spectral orders showing sufficient well-resolved lines in regions unaffected by tellurics from Earth’s atmosphere. To save computing time, only the two observations closest to positions of quadrature were used. This approach resulted 036 30 spectral orders showing adequate fits with well defined minima in the sum of the square residuals. Fig.7 shows the result of this method applied to order 66, which demonstrates the effectiveness of optimizing the normalization of the observed spectra in the fitting routine for an order where line blending is significant. The resulting values for ℓB/ℓA after applying this method to the selected orders are shown in Fig.8, where each result is the average of the result from each of the two observations used. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 5 SPECTRAL ANALYSIS 5.1 Atmospheric parameters Since we planned to apply the method of SPD to extract the individual spectra of the components, normalisation of the observed spectra was of critical importance. We used a different approach than that in Section 4, where we extracted RVs. Here, we used the dedicated code described in Kolbas et al. (2015). First, the blaze function of échelle orders was fitted with a high-order polynomial function. Then, the normalised échelle orders were merged. When overlapping regions of successive échelle orders are sufficiently long, the very ends were cut off because of their low S/N. Échelle orders containing broad Balmer lines, in which it is not possible to define the blaze function with enough precision, were treated in a special way. For these échelle orders, the blaze function was interpolated from adjacent orders. This produces more reliable normalisation in spectral regions with broad Balmer lines than the usual pipeline procedures. For recent applications of this approach, please see Pavlovski et al. (2018, 2023); Lester et al. (2019, 2022); Wang et al. (2020, 2023). Overall, a slight difference in line depth between the two components is seen. The more massive component has deeper lines and slightly faster rotation. The Balmer lines are broadened by Stark broadening, and generally are not sensitive to the rotational broadening. Thus, we first optimised portions of the disentangled spectra free of the Balmer lines, primarily to discern the v sin i values. We then performed optimal fitting of disentangled spectra centred on the Hβ and Hα lines, with fixed v sin i . v i v i The light ratio from the optimal fitting of the wings of Hβ and Hγ lines, spectral segments containing only metal lines, and the Mg I triplet at around 5180 Å, are 1.315±0.018, 1.304±0.025 and 1.278±0.033 (Table 3). All three values for the light ratio are consistent within their 1σ uncertainties, with the light ratio determined from the wings of Hβ line being the most precise of the three. Guo et al. (2016) determined atmospheric parameters from tomographically reconstructed spectra of the components. Their spectra cover the wavelength range 3930–4610 Å at a medium spectral resolution of R = 6000. The analysis by Guo et al. (2016) was similar to ours, but with an important difference that they fitted complete separated spectra, whilst we concentrate on the wings of Balmer lines. Our results for the Balmer lines corroborate their findings to within 1σ. We also find good agreement for the v sin i values, which are also within 1σ for both components. Guo et al. (2016) obtained a mean light ratio 1.34±0.03 from spectra centred at 4275 Å – this is slightly larger than but still consistent with our own results. v i 5.2 Direct fitting for the light ratio We were initially unable to constrain the light ratio of the system from the photometric analysis alone (see Section 3), so estimated it using several independent methods. The first method was the TODCOR light ratio reported in Table 2, the second was the optimal fitting of the disentangled spectra (ODS), and a third method is developed here. We repeated the process for all spectral orders showing sufficient well-resolved lines in regions unaffected by tellurics from Earth’s atmosphere. To save computing time, only the two observations closest to positions of quadrature were used. This approach resulted 036 30 spectral orders showing adequate fits with well defined minima in the sum of the square residuals. Fig.7 shows the result of this method applied to order 66, which demonstrates the effectiveness of optimizing the normalization of the observed spectra in the fitting routine for an order where line blending is significant. The resulting values for ℓB/ℓA after applying this method to the selected orders are shown in Fig.8, where each result is the average of the result from each of the two observations used.

Physical Properties of the Eclipsing Binary KIC 9851944: Spectral Analysis

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 7 PHYSICAL PROPERTIES We determined the physical properties of KIC 9851944 from the spectroscopic and photometric results obtained above. For this we used the JKTABSDIM code (Southworth et al. 2005), modified to use the IAU system of nominal solar values (Prša et al. 2016) plus the NIST 2018 values for the Newtonian gravitational constant and the Stefan-Boltzmann constant. Errorbars were propagated via a perturbation analysis. The results are given in Table. 7. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 7 PHYSICAL PROPERTIES We determined the physical properties of KIC 9851944 from the spectroscopic and photometric results obtained above. For this we used the JKTABSDIM code (Southworth et al. 2005), modified to use the IAU system of nominal solar values (Prša et al. 2016) plus the NIST 2018 values for the Newtonian gravitational constant and the Stefan-Boltzmann constant. Errorbars were propagated via a perturbation analysis. The results are given in Table. 7.

Physical Properties of the Eclipsing Binary KIC 9851944: Physical Properties

Physical Properties of the Eclipsing Binary KIC 9851944: Abstract and Intro

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations APPENDIX A: EPHEMERIS DETERMINATION This appendix presents the results from the preliminary JKTEBOP analaysis as well as times of primary minimum and fitted values calculated in section 3. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations APPENDIX A: EPHEMERIS DETERMINATION This appendix presents the results from the preliminary JKTEBOP analaysis as well as times of primary minimum and fitted values calculated in section 3.

Physical properties of the eclipsing binary KIC 9851944: Appendix A: Ephemeris Determination

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 8 ASTEROSEISMIC ANALYSIS 8.1 Frequency analysis Following the binary modelling, we continue with the asteroseismic analysis of the target. Because the observed pulsations have much smaller amplitudes than the binary signal, the quality of the TESS and WASP data are insufficient for the asteroseismic analysis, and we limit ourselves to using the residual Kepler light curve. This is the merged light curve of all available Kepler short-cadence data after subtracting the best-fitting binary model, hereafter referred to as the pulsation light curve. To minimize the impact of outliers and instrumental effects on the asteroseismic analysis of small-amplitude pulsations, we apply additional processing to the data. Firstly, we remove those parts where coronal mass ejections (CMEs) or thermal and pointing changes of the spacecraft, such as at the start of a quarter or after a safe-mode event, have a visible impact on the quality of the light curve. Secondly, we apply preliminary iterative pre-whitening (as described by, e.g., Van Reeth et al. 2023) to build a tentative mathematical model of the 20 most dominant pulsations using a sum of sine waves 8.2 Tidal perturbation analysis 8.3 Orbital harmonic frequencies As pointed out by Guo et al. (2016), the detection of an orbital harmonic frequency comb is somewhat unexpected for synchronised binaries with circular orbits, though it has also been detected for other such systems (da Silva et al. 2014). Because the orbital eccentricity of the binary is zero, the equilibrium tides that are responsible for deforming the star and perturbing the pulsations are considerably larger than the dynamical tides that excite oscillations. Hence, this can indicate that this system has a slightly eccentric orbit or that one or both of the components is asynchronously rotating, within the uncertainty margins of our measurements. 8.4 Gravity-mode period-spacing pattern The error margins are smaller than the symbol sizes. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 8 ASTEROSEISMIC ANALYSIS 8.1 Frequency analysis Following the binary modelling, we continue with the asteroseismic analysis of the target. Because the observed pulsations have much smaller amplitudes than the binary signal, the quality of the TESS and WASP data are insufficient for the asteroseismic analysis, and we limit ourselves to using the residual Kepler light curve. This is the merged light curve of all available Kepler short-cadence data after subtracting the best-fitting binary model, hereafter referred to as the pulsation light curve. To minimize the impact of outliers and instrumental effects on the asteroseismic analysis of small-amplitude pulsations, we apply additional processing to the data. Firstly, we remove those parts where coronal mass ejections (CMEs) or thermal and pointing changes of the spacecraft, such as at the start of a quarter or after a safe-mode event, have a visible impact on the quality of the light curve. Secondly, we apply preliminary iterative pre-whitening (as described by, e.g., Van Reeth et al. 2023) to build a tentative mathematical model of the 20 most dominant pulsations using a sum of sine waves 8.2 Tidal perturbation analysis 8.3 Orbital harmonic frequencies As pointed out by Guo et al. (2016), the detection of an orbital harmonic frequency comb is somewhat unexpected for synchronised binaries with circular orbits, though it has also been detected for other such systems (da Silva et al. 2014). Because the orbital eccentricity of the binary is zero, the equilibrium tides that are responsible for deforming the star and perturbing the pulsations are considerably larger than the dynamical tides that excite oscillations. Hence, this can indicate that this system has a slightly eccentric orbit or that one or both of the components is asynchronously rotating, within the uncertainty margins of our measurements. 8.4 Gravity-mode period-spacing pattern The error margins are smaller than the symbol sizes.

Physical Properties of the Eclipsing Binary KIC 9851944: Asteroseismic Analysis

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 6 ANALYSIS OF THE LIGHT CURVE The components of KIC 9841944 are close to each other and thus have a significant tidal deformation. We therefore sought to model the light curve using a code that is based on Roche geometry. We selected the Wilson-Devinney (WD) code (Wilson & Devinney 1971; Wilson 1979) for this, and used the 2004 version of the code driven using the JKTWD wrapper (Southworth et al. 2011). The user guide which accompanies the WD code (Wilson & Van Hamme 2004) includes a description of all input and output quantities discussed below. The WD code is computationally expensive and is not suited to the analysis of the full 500 000 short-cadence datapoints in one step. We therefore used the orbital ephemeris determined in Section 3 to convert the datapoints to orbital phase, and then binned them into a much smaller number of points. We chose a bin size of 0.001 orbital phases during the eclipses and 0.005 outside the eclipses, resulting in a total of 456 phase-binned datapoints suitable for analysis with the WD code. Through a process of trying a large number of different fits with a range of fixed and fitted parameters, we arrived at a good solution to the light curve. We adopt this as the default solution, plot it in Fig. 9, and give the fitted parameters in Table 6. It was obtained in Mode = 0 with the following fitted parameters: the light contributions of the two stars; their potentials; their gravity darkening coefficients; the linear coefficients of the logarithmic LD law; and the orbital inclination. We fixed the mass ratio at the spectroscopic value, the orbital eccentricity to zero, the albedos to 1.1, the logarithmic coefficients of LD to values from Van Hamme (1993), third light to zero, and the rotation rates to synchronous. We adopted the maximum numerical precision values of N1 = N2 = 60 and N1L = N2L = 60, the simple treatment of reflection, and the Cousins R-band as a proxy for the Kepler passband. This solution gives a light ratio in excellent agreement with the spectroscopic values in Table 4. The fractional radii in Table 6 are volume-equivalent values calculated by the LC component of the WD code. For the determination of the uncertainties of the fitted parameters we ran a wide range of solutions with a large number of different possible approaches to modelling the light curve (see Southworth 2020). We calculated the errorbar for each parameter by adding in quadrature the contribution from every model choice, which in turn was taken to be the amount that parameter changed by versus the default solution. The following different approaches were explored. (i) We varied the amount of binning prior to the WD solution, finding that it had a negligible effect on the fitted parameters. (ii) We changed the numerical precision to N1 = N2 = N1L = N2L = 50. This modified the fractional radii by a maximum of 0.5%. (iii) We tried the detailed reflection effect and found almost identical results. (iv) Attempts to fit for mass ratio returned a value very close to the spectroscopic one and almost no change in the other parameters. (v) Fitting for the rotation rates of the stars yielded values close to and consistent with synchronous rotation and little change in the other parameters. (vi) Fitting for albedo gave a poorer fit but again very little change in the other parameters. (vii) Allowing for third light gave very similar parameters and a very small amount of third light consistent with zero. (viii) Fitting for one of the temperatures of the stars instead of the two light contributions directly (namely Mode = 2) changed the fractional radii by 0.2%. (ix) Fixing the limb darkening coefficients to theoretical values changed the fractional radii by 0.5%. (x) Using the square-root instead of logarithmic limb darkening law had a negligible effect. The best-fitting parameters were highly robust against all these experiments. The only significantly discrepant fit (neglecting our original exploratory ones) was when we used the Cousins I-band instead of the R-band. However, we are able to reject this fit as it is not consistent with the spectroscopic light ratio. The calculated uncertainties are given in Table 6. Fig. 9 shows that the best fit is extremely good, with an r.m.s. of 0.20 mmag, but that there are systematics remaining in the residuals. We attribute these to the WD numerical integration for points during the eclipses, and residual pulsations for points outside eclipse. Our approach of phase-binning the data gives a light curve practially without Poisson noise, so makes any imperfections in the fit easily noticable. We note that the systematics in the residuals of the fits we found to the data are too small to show up in plots of unbinned data, so may well be present in previous work on this object. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 6 ANALYSIS OF THE LIGHT CURVE The components of KIC 9841944 are close to each other and thus have a significant tidal deformation. We therefore sought to model the light curve using a code that is based on Roche geometry. We selected the Wilson-Devinney (WD) code (Wilson & Devinney 1971; Wilson 1979) for this, and used the 2004 version of the code driven using the JKTWD wrapper (Southworth et al. 2011). The user guide which accompanies the WD code (Wilson & Van Hamme 2004) includes a description of all input and output quantities discussed below. The WD code is computationally expensive and is not suited to the analysis of the full 500 000 short-cadence datapoints in one step. We therefore used the orbital ephemeris determined in Section 3 to convert the datapoints to orbital phase, and then binned them into a much smaller number of points. We chose a bin size of 0.001 orbital phases during the eclipses and 0.005 outside the eclipses, resulting in a total of 456 phase-binned datapoints suitable for analysis with the WD code. Through a process of trying a large number of different fits with a range of fixed and fitted parameters, we arrived at a good solution to the light curve. We adopt this as the default solution, plot it in Fig. 9, and give the fitted parameters in Table 6. It was obtained in Mode = 0 with the following fitted parameters: the light contributions of the two stars; their potentials; their gravity darkening coefficients; the linear coefficients of the logarithmic LD law; and the orbital inclination. We fixed the mass ratio at the spectroscopic value, the orbital eccentricity to zero, the albedos to 1.1, the logarithmic coefficients of LD to values from Van Hamme (1993), third light to zero, and the rotation rates to synchronous. We adopted the maximum numerical precision values of N1 = N2 = 60 and N1L = N2L = 60, the simple treatment of reflection, and the Cousins R-band as a proxy for the Kepler passband. This solution gives a light ratio in excellent agreement with the spectroscopic values in Table 4. The fractional radii in Table 6 are volume-equivalent values calculated by the LC component of the WD code. For the determination of the uncertainties of the fitted parameters we ran a wide range of solutions with a large number of different possible approaches to modelling the light curve (see Southworth 2020). We calculated the errorbar for each parameter by adding in quadrature the contribution from every model choice, which in turn was taken to be the amount that parameter changed by versus the default solution. The following different approaches were explored. (i) We varied the amount of binning prior to the WD solution, finding that it had a negligible effect on the fitted parameters. (ii) We changed the numerical precision to N1 = N2 = N1L = N2L = 50. This modified the fractional radii by a maximum of 0.5%. (iii) We tried the detailed reflection effect and found almost identical results. (iv) Attempts to fit for mass ratio returned a value very close to the spectroscopic one and almost no change in the other parameters. (v) Fitting for the rotation rates of the stars yielded values close to and consistent with synchronous rotation and little change in the other parameters. (vi) Fitting for albedo gave a poorer fit but again very little change in the other parameters. (vii) Allowing for third light gave very similar parameters and a very small amount of third light consistent with zero. (viii) Fitting for one of the temperatures of the stars instead of the two light contributions directly (namely Mode = 2) changed the fractional radii by 0.2%. (ix) Fixing the limb darkening coefficients to theoretical values changed the fractional radii by 0.5%. (x) Using the square-root instead of logarithmic limb darkening law had a negligible effect. The best-fitting parameters were highly robust against all these experiments. The only significantly discrepant fit (neglecting our original exploratory ones) was when we used the Cousins I -band instead of the R -band. However, we are able to reject this fit as it is not consistent with the spectroscopic light ratio. The calculated uncertainties are given in Table 6. I R Fig. 9 shows that the best fit is extremely good, with an r.m.s. of 0.20 mmag, but that there are systematics remaining in the residuals. We attribute these to the WD numerical integration for points during the eclipses, and residual pulsations for points outside eclipse. Our approach of phase-binning the data gives a light curve practially without Poisson noise, so makes any imperfections in the fit easily noticable. We note that the systematics in the residuals of the fits we found to the data are too small to show up in plots of unbinned data, so may well be present in previous work on this object.

Physical Properties of the Eclipsing Binary KIC 9851944: Analysis of the Light Curve

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 9 DISCUSSION After the best matching coeval isochrone was found, we removed the corresponding metallicity value from our isochrone grid and repeated the procedure to explore the effect of varying [Fe/H] on the predicted age of the system. We did this twice, leading to first, second, and third best matching coeval isochrones, each corresponding to a different metallicity. For method 1, the metallicity values of the first, second and third best matching isochrones were 0.05, 0.1 and 0.0 dex, respectively. For method 2, these values correspond to 0.05, 0.0 and 0.1 dex, i.e., the same set, except the second and third best matches are swapped. In all cases, the predicted age of the system is 1.259 ± 0.073 Gyr. The error on this age estimate is taken to be half the average grid spacing either side of the best fitting isochrone. We note that this age estimation is in excellent agreement with the estimation by Guo et al. (2016) of 1.25 Gyr. A possible explanation is that the secondary star is approaching the terminal age main sequence (TAMS), i.e., an evolved stage where the sensitivity of the models increases. Thus, a full evolutionary modelling analysis, such as that carried out by Guo et al. (2016), might yield better model predictions because metallicity and other parameters relating to, e.g., overshooting, are included as free parameters so the model is more flexible. This would also provide the means for a more detailed discussion of model comparison with observations but is beyond the scope of this paper. Here, we simply searched in grids of pre-computed models, which are limited by the size of the grid steps in metallicity and age, as well as fixed input physics, to estimate the age of the system. The parameter ranges that the isochrone neighbours span in the planes shown in Fig.15 (i.e., about twice the uncertainty), and the agreement with the value determined by Guo et al. (2016), is evidence that this age estimation is accurate. We present the results previously derived by Guo et al. (2016) in Table 8. The 13 spectroscopic observations that the previous authors used to derive their RVs and atmospheric parameters have resolutions of R = 6000. While it was already discussed in section 5.1 that our atmospheric parameters agree with those derived by Guo et al. (2016), the higher resolution (R = 60000) of the spectra, and larger number of observations (33) used in this work yielded a much higher quality RV curve. This is reflected by comparing the reported masses between the two studies; the precision in the mass estimates by Guo et al. (2016) are 4.0% and 3.9% for the primary and secondary, respectively, while we attain precisions of 0.57% and 0.59%. The two sets of mass measurements agree to within 0.2σ for MA and 1.1σ for MB. In contrast, our radius measurements differ from those of Guo et al. (2016) by 6.6σ for star A and 3.5σ for star B. However, Guo et al. (2016) note the discrepancy between their values for the radius ratio derived from two spectroscopic methods (k = 1.22 ± 0.05 and k = 1.27 ± 0.29) and the value derived from their light curve modelling (1.41 ± 0.018), where they tentatively adopt the radii associated to the latter. The radius ratio derived here agrees with the values derived by Guo et al. (2016) using their spectroscopic methods within 0.8 σ and 0.3 σ, but shows the same discrepancy compared to the value derived from their light curve modelling, for which Guo et al. (2016) note there exists a family of comparable solutions due to the partial nature of the eclipses. We have assumed a circularised and synchronized orbit for KIC 9851944 because our RV- and light-curve solutions were consistent with a circular orbit, and the values for the component of the synchronous velocity along our line of site are in excellent agreement with the values for vsini derived in section 5.1 (see Table 3). Further justification was provided by Guo et al. (2016) where they examined the eclipse times by Conroy et al. (2014) and Gies et al. (2015), finding that the median deviation of the phase difference between the primary and secondary eclipses from that of a circular orbit suggests e ≥ 0.0001. Furthermore, the circularization timescale for a binary system like KIC 9851944 is 600 Myr, and the synchronization timescale is an order of magnitude shorter (Zahn 1977; Khaliullin & Khaliullina 2010; Guo et al. 2016); these timescales are shorter than the age of the system reported by both studies. Our estimations of the near-core and surface rotations reported in section 5.1 and 8 suggest KIC 9851944 is rotating rigidly and synage. The locations of the isochrones within those planes corresponding to the model with the smallest quadrature distance to the measured quantities that were included in the objective function are indicated for those from both model comparison methods. chronously. This is similar to the findings by e.g. Guo et al. (2017); Guo & Li (2019), that the short-period EBs KIC 9592855 and KIC 7385478 both contain a γ Doradus pulsator that is tidally synchronised at the surface as well as at the near-core regions. In contrast to the above, our detection of the orbital harmonic frequency comb for KIC 9851944 either suggests asynchronous rotation or a non-zero eccentricity that has not been detected due to observational error because these can be indicative of tidally excited oscillations, It is unclear whether the very small value for the lower limit on the eccentricity, i.e., e > 0.0001, reported above for this system would be enough to induce tidal excitation of modes at the amplitudes observed here. The pulsation analysis here complements the study by Guo et al. (2016) who report splittings to some of the pulsation modes. We confirm the detection of tidally split multiplets, and explain their origin; we present evidence to suggest that these are due to pertubations to the pulsation mode cavaties, i.e., tidally perturbed pulsations, by investigating their phase and amplitude dependencies with orbital phase. Guo et al. (2016) attempted to interpret the modes by comparing the observations with theoretically computed frequencies, from which they concluded that the observations can be explained by low-order p modes in the primary and the secondary, or the g mode and mixed modes of the secondary (Guo et al. 2016). We confirm that some of the p modes belong to the primary, and others could belong to either the primary or the secondary from modulation of the amplitudes during eclipses. Our evidence suggests the primary is the hybrid, and this is because the saw-tooth-like “glitch“ in pulsation phase of the tidally perturbed g mode (see Fig. C1) can only be explained if the pulsation belongs to the primary (Van Reeth et al. 2022, 2023). Guo et al. (2016) report that mode identification was inconclusive, reflecting the difficulty in identifying the p-modes in δ Scuti stars. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 9 DISCUSSION After the best matching coeval isochrone was found, we removed the corresponding metallicity value from our isochrone grid and repeated the procedure to explore the effect of varying [Fe/H] on the predicted age of the system. We did this twice, leading to first, second, and third best matching coeval isochrones, each corresponding to a different metallicity. For method 1, the metallicity values of the first, second and third best matching isochrones were 0.05, 0.1 and 0.0 dex, respectively. For method 2, these values correspond to 0.05, 0.0 and 0.1 dex, i.e., the same set, except the second and third best matches are swapped. In all cases, the predicted age of the system is 1.259 ± 0.073 Gyr. The error on this age estimate is taken to be half the average grid spacing either side of the best fitting isochrone. We note that this age estimation is in excellent agreement with the estimation by Guo et al. (2016) of 1.25 Gyr. A possible explanation is that the secondary star is approaching the terminal age main sequence (TAMS), i.e., an evolved stage where the sensitivity of the models increases. Thus, a full evolutionary modelling analysis, such as that carried out by Guo et al. (2016), might yield better model predictions because metallicity and other parameters relating to, e.g., overshooting, are included as free parameters so the model is more flexible. This would also provide the means for a more detailed discussion of model comparison with observations but is beyond the scope of this paper. Here, we simply searched in grids of pre-computed models, which are limited by the size of the grid steps in metallicity and age, as well as fixed input physics, to estimate the age of the system. The parameter ranges that the isochrone neighbours span in the planes shown in Fig.15 (i.e., about twice the uncertainty), and the agreement with the value determined by Guo et al. (2016), is evidence that this age estimation is accurate. We present the results previously derived by Guo et al. (2016) in Table 8. The 13 spectroscopic observations that the previous authors used to derive their RVs and atmospheric parameters have resolutions of R = 6000. While it was already discussed in section 5.1 that our atmospheric parameters agree with those derived by Guo et al. (2016), the higher resolution (R = 60000) of the spectra, and larger number of observations (33) used in this work yielded a much higher quality RV curve. This is reflected by comparing the reported masses between the two studies; the precision in the mass estimates by Guo et al. (2016) are 4.0% and 3.9% for the primary and secondary, respectively, while we attain precisions of 0.57% and 0.59%. The two sets of mass measurements agree to within 0.2σ for MA and 1.1σ for MB. In contrast, our radius measurements differ from those of Guo et al. (2016) by 6.6σ for star A and 3.5σ for star B. However, Guo et al. (2016) note the discrepancy between their values for the radius ratio derived from two spectroscopic methods (k = 1.22 ± 0.05 and k = 1.27 ± 0.29) and the value derived from their light curve modelling (1.41 ± 0.018), where they tentatively adopt the radii associated to the latter. The radius ratio derived here agrees with the values derived by Guo et al. (2016) using their spectroscopic methods within 0.8 σ and 0.3 σ, but shows the same discrepancy compared to the value derived from their light curve modelling, for which Guo et al. (2016) note there exists a family of comparable solutions due to the partial nature of the eclipses. We have assumed a circularised and synchronized orbit for KIC 9851944 because our RV- and light-curve solutions were consistent with a circular orbit, and the values for the component of the synchronous velocity along our line of site are in excellent agreement with the values for v sin i derived in section 5.1 (see Table 3). Further justification was provided by Guo et al. (2016) where they examined the eclipse times by Conroy et al. (2014) and Gies et al. (2015), finding that the median deviation of the phase difference between the primary and secondary eclipses from that of a circular orbit suggests e ≥ 0.0001. Furthermore, the circularization timescale for a binary system like KIC 9851944 is 600 Myr, and the synchronization timescale is an order of magnitude shorter (Zahn 1977; Khaliullin & Khaliullina 2010; Guo et al. 2016); these timescales are shorter than the age of the system reported by both studies. v i Our estimations of the near-core and surface rotations reported in section 5.1 and 8 suggest KIC 9851944 is rotating rigidly and synage. The locations of the isochrones within those planes corresponding to the model with the smallest quadrature distance to the measured quantities that were included in the objective function are indicated for those from both model comparison methods. chronously. This is similar to the findings by e.g. Guo et al. (2017); Guo & Li (2019), that the short-period EBs KIC 9592855 and KIC 7385478 both contain a γ Doradus pulsator that is tidally synchronised at the surface as well as at the near-core regions. In contrast to the above, our detection of the orbital harmonic frequency comb for KIC 9851944 either suggests asynchronous rotation or a non-zero eccentricity that has not been detected due to observational error because these can be indicative of tidally excited oscillations, It is unclear whether the very small value for the lower limit on the eccentricity, i.e., e > 0.0001, reported above for this system would be enough to induce tidal excitation of modes at the amplitudes observed here. e > The pulsation analysis here complements the study by Guo et al. (2016) who report splittings to some of the pulsation modes. We confirm the detection of tidally split multiplets, and explain their origin; we present evidence to suggest that these are due to pertubations to the pulsation mode cavaties, i.e., tidally perturbed pulsations, by investigating their phase and amplitude dependencies with orbital phase. Guo et al. (2016) attempted to interpret the modes by comparing the observations with theoretically computed frequencies, from which they concluded that the observations can be explained by low-order p modes in the primary and the secondary, or the g mode and mixed modes of the secondary (Guo et al. 2016). We confirm that some of the p modes belong to the primary, and others could belong to either the primary or the secondary from modulation of the amplitudes during eclipses. Our evidence suggests the primary is the hybrid, and this is because the saw-tooth-like “glitch“ in pulsation phase of the tidally perturbed g mode (see Fig. C1) can only be explained if the pulsation belongs to the primary (Van Reeth et al. 2022, 2023). Guo et al. (2016) report that mode identification was inconclusive, reflecting the difficulty in identifying the p-modes in δ Scuti stars.

Physical Properties of the Eclipsing Binary KIC 9851944: Discussion

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 3 ORBITAL EPHEMERIS We also fitted for the linear coefficients u of the quadratic limb darkening (LD) law while quadratic terms were taken from Claret & Bloemen (2011) and Claret (2017) for the Kepler and TESS bands, respectively; we used the estimated atmospheric parameters reported in the KIC to choose the appropriate values for the quadratic coefficients. We performed fits to the WASP light curve but the lower quality of this data compared to the Kepler and TESS light curves means that including these results in the calculation of the overall preliminary eclipse model would lead to less well determined parameters. Thus, we simply include the epochs of primary minimum estimated from the WASP light curves as additional observational constraints on T0 in the preliminary fits to the Kepler and TESS light curves. The adopted values for the light curve parameters from this preliminary analysis were taken as the weighted averages of the results from the fits to the individual Kepler quarters and TESS sectors, where the reciprocal of the squared errors from the covriance matrix were used as weights. These values are given in Table A1, and Fig. 2 shows the fit to the Kepler Quarter 0 light curve; high-frequency variability due to pulsations of the δ Scuti type is clearly visible at all orbital phases in the residuals shown in the lower panel, with amplitudes of around ±0.2 mmag. There seems to be a trend in the O-C diagram compared with the linear ephemeris, with data points corresponding to TESS observations (>1000 cycles) all appearing above the red line. Thus, we also attempted to fit for a quadratic ephemeris, which is represented by the blue dashed line in Fig. 3. However, the corresponding quadratic term was a similar size to its errorbar. We therefore decided to stick with the linear ephemeris which was measured to be: This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 3 ORBITAL EPHEMERIS We also fitted for the linear coefficients u of the quadratic limb darkening (LD) law while quadratic terms were taken from Claret & Bloemen (2011) and Claret (2017) for the Kepler and TESS bands, respectively; we used the estimated atmospheric parameters reported in the KIC to choose the appropriate values for the quadratic coefficients. We performed fits to the WASP light curve but the lower quality of this data compared to the Kepler and TESS light curves means that including these results in the calculation of the overall preliminary eclipse model would lead to less well determined parameters. Thus, we simply include the epochs of primary minimum estimated from the WASP light curves as additional observational constraints on T0 in the preliminary fits to the Kepler and TESS light curves. The adopted values for the light curve parameters from this preliminary analysis were taken as the weighted averages of the results from the fits to the individual Kepler quarters and TESS sectors, where the reciprocal of the squared errors from the covriance matrix were used as weights. These values are given in Table A1, and Fig. 2 shows the fit to the Kepler Quarter 0 light curve; high-frequency variability due to pulsations of the δ Scuti type is clearly visible at all orbital phases in the residuals shown in the lower panel, with amplitudes of around ±0.2 mmag. There seems to be a trend in the O-C diagram compared with the linear ephemeris, with data points corresponding to TESS observations (>1000 cycles) all appearing above the red line. Thus, we also attempted to fit for a quadratic ephemeris, which is represented by the blue dashed line in Fig. 3. However, the corresponding quadratic term was a similar size to its errorbar. We therefore decided to stick with the linear ephemeris which was measured to be:

Physical Properties of the Eclipsing Binary KIC 9851944: Orbital Ephemeris

In a bold market forecast, a top analyst has predicted that the licensed A.I exchange token, , is on track to eclipse giants such as Solana (SOL) and Cardano (ADA) in the near future. This projection is underpinned by the innovative technology and strategic market positioning of Option2Trade (O2T), coupled with a Tier-1 listing on the horizon, signaling a significant shift in the crypto exchange landscape. Option2Trade (O2T) meet the needs of both seasoned traders and newcomers to the market. With its user-friendly interface, intuitive trading solutions, and a focus on social trading and learning, Option2Trade (O2T) is poised to appeal to a broad audience, enhancing ecosystem liquidity and usability. This holistic approach to trading, combined with the token's technological backbone, set the stage for Option2Trade’s (O2T) ascent in the crypto market. Option2Trade (O2T) distinguishes itself through its licensed A.I exchange platform designed to Unique Selling Points of O2T What sets apart from established tokens like and Cardano (ADA) are its unique selling points that cater to evolving market demand for more integrated and intelligent trading platforms. Option2Trade’s (O2T) use of artificial intelligence to optimize trading strategies and risk management, along with its commitment to building a robust and engaging ecosystem through staking and governance participation, highlights its potential to offer more than just a trading platform but a comprehensive trading experience. Option2Trade (O2T) Solana (SOL) Market Trends Supporting O2T's Forecasted Success The prediction of Option2Trade (O2T) eclipsing Solana (SOL) and is supported by current market trends that favor platforms offering advanced technological solutions and user engagement. The growing interest in AI-driven trading, coupled with the increasing importance of community governance in the crypto space, aligns with Option2Trade’s (O2T) core offerings. These trends, combined with the anticipation of a Tier-1 listing, bolster the analyst's forecast of Option2Trade’s (O2T) rising prominence in the crypto exchange market. Cardano (ADA) The Implications of a Tier-1 Listing A Tier-1 listing for would not only validate its market potential but also enhance its visibility and accessibility to a wider investor audience. Such a listing is considered a hallmark of credibility and stability in the crypto world, and for  Option2Trade (O2T), it represents a critical step toward achieving its ambitious goals. This milestone could significantly impact  Option2Trade (O2T)’s market performance, positioning it as a formidable competitor to established tokens like Solana (SOL) and Cardano (ADA). Option2Trade (O2T) O2T's Potential to Outperform Established Tokens The strong belief in Option2Trade's (O2T) potential to outperform giants such as Solana (SOL) and Cardano (ADA) is rooted in its strategic approach to blending cutting-edge technology with a user-centric trading platform. By offering features that support network governance, enable staking, and facilitate social trading, Option2Trade (O2T) is not just creating a token but cultivating a vibrant ecosystem that responds to the needs and preferences of its users. In conclusion, the top analyst's prediction that will eclipse Solana (SOL) and Cardano (ADA) reflects a broader shift towards valuing platforms that offer technological sophistication, market inclusivity, and community engagement with its licensed A.I exchange platform, user-friendly features, and a Tier-1 listing on the horizon, Option2Trade (O2T) is well-positioned to secure a prominent place in the crypto exchange market, challenging established tokens and reshaping investor expectations for what a trading platform can offer. As the market continues to evolve, Option2Trade’s (O2T) focus on innovation and user experience sets a new benchmark for success in the cryptocurrency space. Option2Trade (O2T) For more information on the Option2Trade (O2T) Presale: Use promo code O2TLaunch to get a 15% bonus Visit Option2Trade (O2T) Join and become a community member: | Telegram Twitter This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

Top Analyst Predicts Licensed A.I Exchange Token Will Eclipse Solana and Cardano

শীর্ষ বিশ্লেষক ভবিষ্যদ্বাণী করেছেন লাইসেন্সপ্রাপ্ত A.I এক্সচেঞ্জ টোকেন সোলানা এবং কার্ডানোকে গ্রহণ করবে

Top-Analysten sagen voraus, dass der lizenzierte A.I Exchange Token Solana und Cardano in den Schatten stellen wird

Un importante analista predice que el token de intercambio de IA con licencia eclipsará a Solana y Cardano

Un analyste de premier plan prédit que le jeton d'échange A.I sous licence éclipsera Solana et Cardano

शीर्ष विश्लेषक ने भविष्यवाणी की है कि लाइसेंस प्राप्त एआई एक्सचेंज टोकन सोलाना और कार्डानो पर ग्रहण लगा देगा

トップアナリストは、認可されたA.I交換トークンがSolanaとCardanoを凌駕すると予測

최고 분석가는 라이선스가 부여된 AI 교환 토큰이 Solana와 Cardano를 잠식할 것으로 예측합니다.

Principais analistas prevêem que token de troca de IA licenciado irá eclipsar Solana e Cardano

Ведущий аналитик прогнозирует, что лицензированный токен AI Exchange затмит Solana и Cardano

En İyi Analistler, Lisanslı Yapay Zeka Değişim Tokenının Solana ve Cardano'yu gölgede bırakacağını tahmin ediyor

Nhà phân tích hàng đầu dự đoán Token trao đổi A.I được cấp phép sẽ làm lu mờ Solana và Cardano

顶级分析师预测获得许可的 A.I 交易所代币将超越 Solana 和 Cardano

Lawyers, accountants, and marketers can learn a lot from Fortnite and Donkey Kong Too many professional services firms are approaching today’s world of -like complexity with strategies more appropriate for Donkey Kong. Fortnite Here’s what’s going on. Web1 games and arcades The concept behind the classic video game Donkey Kong is simple. For 25 cents at an arcade, a player could guide the hero, , up the girders and ladders of a construction site to rescue the distressed Pauline, who was being held hostage by Donkey Kong, a giant scary ape (though actually the protagonist if you know the actual backstory). Along the way, poor Mario would have to jump over or destroy obstacles like barrels and fireballs that Donkey Kong at the top of the steel tower rolled his way. Mario The whole thing took place in two highly pixelated dimensions and four colors, pitting the player against the CPU-controlled ape. The game was the cutting-edge star of the early 1980s vibrant arcade scene. By today’s standards, it looks painfully dated, but the franchise became a cultural icon, spawning films, and fashion trends. The podcast Acquired has a great rundown of the game in the context of the history of . We can’t Nintendo recommend it enough. Enter... more players Fortnite, in contrast, is much more sophisticated. And Acquired does a great job with that larger . story too The game presents three-dimensional hybrid worlds featuring zombies, traps, and fights to the death against dozens of live players who are simultaneously online anywhere around the world. Within months of its release, Fortnite — which can be played on any major computer or gaming format — had become a true cultural . Four years later, it remains on the cutting edge. phenomenon We could keep going with the evolution of this process by tackling Roblox, but let’s keep it to these two. So, you’re going to extend the analogy to lawyers? Now let’s apply this contrast to our industry (marketing) and other professional services like fund administration for instance. In fact, let’s focus on fund services where interesting things are afoot. Fund services are generally the outsourced partner accounting and operations work of venture capital and private equity firms. One can view fund services as a series of individual workflows. Composing these workflows are many tasks related to each stage of a particular service. Each stage may include numerous financial reviews and revisions, like attorney approvals, name corrections, and adding phrases here or there to documents, before the service can move forward to its completion. The process is like an assembly line. It’s Donkey Kong. Serial work vs. parallel processing Automation makes the process easier and faster. An assembly line worker who has been manually installing rivets will be more productive with an electric rivet gun. It’s much more efficient. But it’s still an assembly line. It still requires the worker responsible for one stage of service to complete the job before passing his work along to the next stage. It’s still Donkey Kong. Most financial services software reflects the pitfalls of the single-player approach. Single-player software is helpful because one person can go in, do his or her work, and leave so someone else can enter and complete the next step. It’s orderly. But it’s also limited. Project leaders and team members, for example, don’t necessarily know where things stand, for anything at any given point, unless they are in the application. None of the people involved knows where they are in the workflow until everything is completed. In a multi-player professional service or fund administration model that resembles Fortnite, in contrast, far fewer people must wait for someone else to complete their job before another phase can begin. Numerous people can be in an application or document at the same time. Anyone given access can make necessary changes and everyone can see what everyone is doing. Let the adventure begin! Multiple actors can simultaneously review and amend documents. Everyone is always dealing with the current version of the text. It doesn’t matter if they are internal, external, or from a third party. It doesn’t matter if they are in the same place or working from points scattered around the globe. Everybody sees the dialogue and decisions that are being made in real-time. Monitoring progress is built-in, making it easier to understand the status of a process at any time. It’s Fortnite. In Donkey Kong, Mario could only ever rescue Pauline. In Fortnite, you can do so much more. Real-world Here’s how it applies to fund services for example: In a single-player financial tech application, a financial services provider can see a document that a limited partnership has sent, verify the data and note that lawyers have signed off on it. When it's a multiplayer system, the fund services partner can see and do all of those tasks but also observe delays or problems across limited partnerships and, importantly, intervene and act quickly to fix them. In addition to the fund services partner understanding where tasks are within the flow, they have full audit control with transparency across all parties in the flow. This is where leaders in the industry like 4Pines Fund Services come in. In the software development world, the multiplayer approach is not particularly new. But it was rare in the private equity industry — up until recently. These kinds of changes are generating a buzz as budgets and operations are getting more focused. This is only going to increase. This is also where a co-sourcing approach to data and cutting-edge fund administration tech stack comes in, for example, to fix real problems. You’re working to close communication gaps and fix transparency issues between fund managers, investors, and service providers. Most importantly, you’re helping chief financial officers and general partners to identify bottlenecks and key performance indicators to improve speed and efficiency. The automated system reduces human error significantly at critical points within clients’ workflows. Everything is near-instantly trackable, traceable, and auditable. Fund administrators and private equity managers seeking the most innovative professional services are opting for the multiplayer approach. And this is happening in marketing and legal services. In private capital, it improves communication between general partners, limited partners, and analysts. It efficiently shepherds sub-docs and other correspondence and compliance through quick and efficient approval processes.  It replaces slow pipelines and obstacles with the latest technologies and solutions that keep fund services running flexibly, seamlessly, and dependably. So what about your industry? Are you ready to play at a higher level? You’d better be. Featured photo: Desi Krasteva Gale

Multiplayer Professional Services Are Eclipsing the Single-Player Variety, and It’s About Time

Los servicios profesionales multijugador están eclipsando la variedad de un solo jugador, y ya era hora

Les services professionnels multijoueurs éclipsent la variété solo, et il était temps

मल्टीप्लेयर व्यावसायिक सेवाएँ एकल-खिलाड़ी विविधता को ग्रहण कर रही हैं, और यह समय की बात है

マルチプレイヤーのプロフェッショナル サービスがシングルプレイヤーの種類を追い越しつつあり、そろそろその時が来ています

Os serviços profissionais multijogador estão eclipsando a variedade de jogador único e já era hora

Các dịch vụ chuyên nghiệp nhiều người chơi đang làm lu mờ sự đa dạng của một người chơi và đã đến lúc

多人游戏专业服务正在让单人游戏种类黯然失色，现在正是时候

In this article, we will see how can we perform operations with database using JDBC. We will then cover how to create a table in MySQL database and how to insert, update, and delete the rows using the JDBC program. crud MySQL You can find a video below with my step-by-step process. youtube https://www.youtube.com/watch?v=fUBoDErcDHA&embedable=true&transcript=true Let’s dive straight into the code Insert Class import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
import java.sql.Statement;
public class Insert {
    public static void main(String[] args) {
        //load the driver
        try {
            Class.forName("com.mysql.cj.jdbc.Driver");
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
        try (Connection con = DriverManager.getConnection("jdbc:mysql://@localhost:3306/new_schema", "root", "root"); Statement st = con.createStatement();) {
            String query = "INSERT INTO EMP(EID,ENAME) values(1,'Ram')";
            int count = st.executeUpdate(query);
            if (count == 0) {
                System.out.println("Record not inserted");
            } else {
                System.out.println("Record Inserted");
            }
        } catch (SQLException se) {
            se.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
} Delete Class import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
import java.sql.Statement;
public class Delete {
    public static void main(String[] args) {
        // load the driver
        try {
            Class.forName("com.mysql.cj.jdbc.Driver");
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
        try (Connection con = DriverManager.getConnection("jdbc:mysql://@localhost:3306/new_schema", "root", "root"); Statement st = con.createStatement();) {
            String query = "DELETE FROM EMP WHERE EID=1";
            int count = st.executeUpdate(query);
            if (count != 0) {
                System.out.println("Record Deleted");
            } else {
                System.out.println("Record Not Deleted");
            }
        } catch (SQLException se) {
            se.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
} Update Class import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
import java.sql.Statement;
public class Update {
    public static void main(String[] args) {
        // load the driver
        try {
            Class.forName("com.mysql.cj.jdbc.Driver");
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
        try (Connection con = DriverManager.getConnection("jdbc:mysql://@localhost:3306/new_schema", "root", "root"); Statement st = con.createStatement();) {
            String query = "UPDATE EMP SET ENAME='SAM' WHERE EID=1";
            int count = st.executeUpdate(query);
            if (count != 0) {
                System.out.println("Record UPDATED");
            } else {
                System.out.println("Record Not UPDATED");
            }
        } catch (SQLException se) {
            se.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
} /Select Class import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
public class Select {
    public static void main(String[] args) {
        // load the driver
        try {
            Class.forName("com.mysql.cj.jdbc.Driver");
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
        try (Connection con = DriverManager.getConnection("jdbc:mysql://@localhost:3306/new_schema", "root", "root"); Statement st = con.createStatement();) {
            String query = "SELECT EID,ENAME FROM EMP";
            ResultSet rs = st.executeQuery(query);
            while (rs.next()) {
                System.out.println(rs.getInt(1) + " " + rs.getString(2));
            }
        } catch (SQLException se) {
            se.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
} The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "database".

Mastering Java JDBC CRUD Operations in Eclipse Using MySQL

Generally, cyber-attacks usually bank on loopholes or vulnerabilities in the security architecture of an ecosystem. Attackers sometimes aim to prove the vulnerabilities in the security of the victims or steal vital information to blackmail victims. What is prominent to most cyber-attacks is that they are usually mounted for financial gains. Blockchain technology is a novel technology that is used to record on-chain transactions in blocks that are concatenated after being verified by miners. Due to its DLT–Decentralized/Distributed ledger Technology—feature, the technology has been widely adopted and utilized by several industries, including finance, healthcare, and logistics. The financial use case of the blockchain brought it to the limelight, which in turn makes it a subject of numerous attacks theoretically. The blockchain is undoubtedly a highly secure network that's almost impossible to attack successfully. This is due to the organic security framework of the technology that secures the network with mining activities. As miners validate transactions on blockchain networks using their hash rate—computing power—the security framework keeps getting robust, making it harder to be susceptible to attacks. The more miners participate in a blockchain, the more secure it becomes because blockchain security is proportionate to the amount of hash rate expended on it. This is why the security of blockchains grows organically. Before diving into the Eclipse attack, let's briefly examine a few terms Sybil Attack A Sybil attack emanates from catfishing. In the blockchain, it involves an attacker operating various nodes to overpower and centralize a blockchain network. The attacker utilizes the various nodes in his control to alter data sent or received on the network Botnet A botnet is a network of internet-connected devices that have been infected with malware which allows an attacker to control the collection of the devices in the network remotely. Also termed "Robot network," it is usually employed by a single attacker to create multiple blockchain nodes to disrupt a blockchain network. A botnet may include servers, PCs, and mobile devices. DDoS A DoS—Denial of Service—attack is a scheme that prevents authorized users from accessing a particular network. It is accomplished by sending malicious requests that cause the victim's resource to malfunction or crash altogether. It can also be carried out by flooding the victim's resources with a tremendous traffic volume. DDoS—Distributed Denial of Service—attack is when a single source faces a DoS attack from multiple malicious machines. It is more harmful than DoS because attacks come from several sources to a single resource. The endemic attack of the blockchain technology is the 51% attack which occurs when a single node controls a 51% hash rate of a particular blockchain. This 51% attack is liable to disrupt the mining activities of such blockchain, which can also facilitate double-spending directly or indirectly. Like many other attacks, the 51% attack is almost impossible due to the financial commitment involved. Due to this impossibility, attackers have sought to employ other lesser attacks to achieve or launch a 51% attack. One of these attacks is the "Eclipse Attack." What is an Eclipse Attack? An eclipse attack can be regarded as a subjective and lesser blockchain attack where a particular node is isolated within the P2P network for such an attack. The attacker obfuscates the isolated node from viewing the legitimate P2P network and starts interacting with the target's node. Inbound connections from other nodes and outbound connections of the target are hindered and redirected to the attacker's nodes. It is called an eclipse attack because the attacker overshadows the target's connection with faux data about the blockchain. How Eclipse Attack Occurs All nodes in a blockchain network are not directly connected; instead, they are interconnected, leaving them to connect with limited neighboring nodes. This makes it possible for an attacker to single out a node from the entire network. Before isolating a node, the attacker would have been in control of malicious nodes that will be connected to the isolated node. These malicious nodes are usually gotten or controlled by the attacker via the Sybil attack or botnet. The attacker then overwhelms the victim with the IP addresses of the malicious nodes waiting for the victim to connect. The need for waiting is because the victim was previously connected to legitimate network nodes; the attacker either waits for the victim to restart their software or forces the victim to do so by launching a DDoS attack. Once the victim restarts their software, in his attempt to reconnect to the network, he falls victim by reconnecting to the malicious nodes with which the victim has been flooded. The malicious nodes are already laying ambush for the victim's node, so the victim easily falls into the web of these malicious nodes leaving the victim at the mercy of these malicious nodes. The victim is now cut off from viewing the broad network and, as such, can be fed with selective or filtered data or even wrong data by the attacker. Effects of Eclipse Attack Block Race In a block race, two miners find a block at the same time; only one will definitely be added to the blockchain while the other is ignored. The ignored block is termed an "orphan block," and the miner that finds it will not get any block reward. An attacker that overshadows multiple miners can cause a block race by hoarding the blocks found by victims and releasing blocks to both the eclipsed and non-eclipsed nodes when a competing block has been discovered. So the eclipsed nodes will just be wasting their computing power. Selfish Mining Selfish Mining is a deceptive mining scheme where a miner or group of miners solves a cryptographic puzzle to open a new block and refuses to publish the block on the public blockchain for others. With selfish mining, the attacker can withhold blocks since the eclipsed nodes are under the attacker's control. He can do this to earn more than his share of block rewards. Two factors control selfish mining by the attacker: -The hash rate of the attacker. -The hash rate of honest miners that mines on the attacker's blocks during the block race. Miner Power Interruption Attackers can fool the victim into wasting their time and computing power to mine orphan blocks that are excluded from the official blockchain. This can be done by the attacker concealing that an eclipsed miner has mined a block. With this, the attacker can boost their hash rate and influence the block-mining contest in their favor. Possible 51% Attack A 51% attack is an endemic blockchain attack that happens when a node has or controls a 51% hash rate on a blockchain network. Since an eclipsed miner is effectively shut out of the legitimate chain, attackers may simultaneously conduct eclipse attacks on several miners in a network to lower the threshold necessary to achieve a successful 51% attack on the network. A 51% attack leads to double-spending attacks. 0-Confirmation Double Spend A zero-confirmation transaction is a transaction that has been initiated but hasn't been confirmed or recorded on the blockchain. Such transaction is not part of the blockchain until miners confirm it on the blockchain though it might be broadcasted. Double spending due to this is termed 0-confirmation double-spending. How? Imagine an NFT broker whose node has been eclipsed by an attacker if the attacker bought NFT from this broker and initiated a transaction. The transaction is broadcasted but not confirmed yet; the broker then releases the NFT hoping the payment will come through. Unfortunately, the transaction wasn't broadcasted. The attacker only publishes the transaction to the victim's node, not to the honest nodes on the network. While the transaction hangs, the attacker will now spend the same digital currency on the legitimate network, where it will be admitted into a block. So if the previous transaction is received, it will be rejected because the asset has been spent. N-Confirmation Double Spend This is similar to the zero-confirmation double-spend, but here, the attacker eclipses the broker's node and miners' nodes. The attacker makes a payment, which is confirmed by the eclipsed nodes and added to the blockchain. Unknown to them, the miners' have been cut off from the legitimate blockchain. So, when they re-join the legitimate network, the block they've previously mined while eclipsed will be orphaned by the original network. How to Extenuate the Possibilities of Eclipse Attack Random selection of nodes: This will reduce the chances of connecting to nodes controlled by attackers whenever nodes want to sync to the network Increase Number of Node connections: If the number of node-to-node connections is increased, it'll also reduce the chances of an eclipse attack. Although, this may have a fallback on the network's efficiency. Whitelisting of miners' nodes to restrict strange nodes. An Eclipse attack might be rare, but it is dangerous if not pre-empt. Ordinarily, it may seem simple since it involves just a single node amongst several nodes on the blockchain. Still, if the attack degenerates into a Sybil attack or a more sophisticated attack, it is no longer an ordinary attack. An eclipse attack can also be a stepping stone for a more deadly attack on a network; this is why it should be forestalled from happening. Conclusion

Breaking Down Eclipse Attacks on a Blockchain

What is an eclipse attack? An eclipse attack is a means of attacking a decentralized network through which an attacker seeks to isolate and attack a specific user or users rather than attack the whole network. In this article, I will explain how some features of , including its uncle block mechanism, could make it more vulnerable to this kind of attack. Ethereum Data structure in Blockchain A blockchain is a decentralized protocol that distributes a database across a number of nodes in its network, establishing a consensus mechanism for past, current and future transactions. The data structure in a blockchain is represented as blocks. In a decentralized blockchain networks, participants agree on what is true instead of a central authority. Blockchains are also transparent and immutable, all network participants can see what is happening on the network, and transaction dates cannot be altered unless by an agreed-upon mechanism. Types of blocks in the Bitcoin blockchain Genesis block The genesis block is the first block of any blockchain, the block is the fundamental starting point of the system and is linked to all future transactions. The world’s first genesis block was created by Satoshi Nakamoto, and in doing so he created Bitcoin. The presence of a genesis block enables a blockchain to generate its transaction history by allowing newly created blocks to be associated with previous blocks. In addition, the genesis block has an important function in synchronizing the nodes of the network — they must all have the same genesis block in their databases. This ensures that the blockchain’s distributed transaction book is the same for everyone, providing security. Main chain blocks Main chain blocks are all those blocks that have been verified and included in a blockchain. To achieve this, a block must achieve consent from the network by having its assigned cryptographic puzzle solved by a node. Once the network has reached consensus, the block is included in the blockchain and propagated by all nodes. In this way, each node of the network has the new block and serves as a verification point for it. Each valid block carries within it a series of transactions that are validated together with that block. For example, in Bitcoin, each carries an average of 2100 transactions. Each transaction in a valid block is confirmed. valid block All valid blocks continue to confirm previous transactions, fully securing all blocks and transaction on the network. Of course, each valid block comprises a data structure allowing verification. The structure includes the block’s hash, its timestamp, its nuncio, and its block transaction data. The process of searching for solution candidates is called mining, with participating nodes being referred to as miners. Each miner that generates a valid solution to a block’s cryptographic puzzle becomes the leader and is allowed to determine the set of unconfirmed transactions. These are then appended to the blockchain Orphan blocks Orphan — or stale — blocks are valid blocks that are not part of a blockchain. These can be created when two miners authenticate blocks at almost the same time, or when an attacker with sufficient hash power tries to reverse a transaction. In these cases, the network consensus protocol comes into play to decide which block will be included in the chain and which will be orphaned. In reality, a blockchain network is programmed to always favor the longest blockchain. That is, it will choose the block that contains more information or processed transactions. In the Bitcoin blockchain, the orphan block is discarded, and work performed by the miner is useless. Additionally, it is because of this type of block that most exchanges and some wallets will automatically wait for additional confirmations before making funds available. They do this by calculating how many blocks were mined between the transaction you are receiving and the current block. >eth.getBlock(blockNumber) } >eth.syncing.highestBlock −eth.getTransaction(“ “).blockNumber How are blocks verified? Blockchain systems function with a Merkle tree data structure, which makes them immutable (in theory). Merkle trees were patented by Ralph Merkle in 1979, the year I was born, and are widely used in cryptography. A Merkle tree is a basic way to verify that shared data was not changed, damaged or altered, and is well-suited to blockchains because it is lightweight. For example, thanks to the Merkle tree structure of Bitcoin, a user’s mobile wallet doesn’t need to download all Bitcoin transactions to validate them. Each transaction in a given block corresponds to a leaf in the Merkle tree, and the Merkle root is built recursively from these by hashing individual transactions. Pairs of transactions (leaves) are hashed to create additional leaf nodes that may correspond to transactions in the blockchain network. Although a Merkle tree can be created from a large number of transactions, the Merkle root always corresponds to a 32-byte or 256-bit string (the SHA256 hashing algorithm, for instance, always outputs a fixed length of 32-bytes, regardless of the size in the input). In this way, we can use something as small as 32-bytes to verify as many as thousands of transactions. Any alteration, no matter how minimal, prevents the verification of the Merkle root, and invalidates part of or all history of the blockchain, according to the case. Types of blocks in the Ethereum Blockchain One of the ways that Ethereum differs to Bitcoin is that it has a faster block creation mechanism, designed to speed up the transaction process. However, when the time interval between block generation is very short (about 15 seconds), an enormous number of orphan blocks can be created, because it is inefficient to include all of these blocks in the blockchain. This caused an issue of miners wasting time working on blocks for no reward. To solve this, Ethereum’s developers launched the GHOST protocol. GHOST stands for ,and the concept is simple. It rewards miners that validate orphan blocks, although the reward is lower than for standard ones. In order to release a reward, the block must be referenced by a block from the main chain — or an uncle block. Greedy Heaviest Observed Subtree An additional advantage of this mechanism is to resolve the problem of network centralization. When blocks are created quickly, it is easy for a large mining pool to monopolize block validation, causing smaller competitors to create an endless amount of useless orphan blocks. Types of Blocks in Ethereum Blockchain There are two types of blocks in Ethereum blockchain: A main chain block is a block that included in the Ethereum blockchain. A miner that finds it is rewarded. An uncle block is a block that is authenticated but does not get included in the main blockchain. The miner gets a lower reward for this than a valid block. Imagine that two miners in different part of the world mine the same block at the same time. They then propagate their blocks across the network. This creates a possible divergence in the chain, so one must be chosen — and the larger block will always win. The unselected block becomes an orphan (in Bitcoin) or an uncle (in Ethereum). Unlike with Bitcoin, an Ethereum miner will receive 1/8 of a full block reward for mining an uncle block. How uncle block rewards work Miners of main chain blocks can reference uncle blocks, and when they do, an additional reward is distributed to both the miner of the main chain block and the miner of the uncle block. Each block included in the main chain can reference up to two uncle blocks and give 1/32 of a full block reward per referenced uncle. However, the reward paid to the miner of the uncle block diminishes over time. By guaranteeing reimbursement for what would otherwise be wasted computational effort, and by making that reward diminish over time, miners on competing chains are motivated to rejoin the main chain. These uncle blocks contribute to chain security and also reduce the probability of 51% attacks. In Ethereum mining pools, can be distributed in a number of ways: Proportional Payout Scheme In this simple scheme, a miner is rewarded proportionally to the number of shares submitted in the time interval between two blocks found by the pool. Consequently, a block reward B is split between the N miners in the pool based on their respective number of shares submitted. Pay-Per-Last-N-Shares (PPLNS) The (PPLNS) scheme distributes a reward proportional to the last N shares that have been submitted. Queue-based Payout Scheme Ethpool3 was the first Ethereum mining pool to introduce a queue-based reward scheme. Under this scheme, miners accumulate credits for each share submitted to the pool operator. Each time a full block is mined by the pool, the block reward is allocated to the miner in the pool with the highest accumulated credit balance. The top miner then has their credit balance reset to the difference between their own and the second highest credit balance in the pool. The referenced relationship creates an extra mint reward, of a full block reward for the regular block owner and for the uncle. The variable ranges from according to the block-height of the reference. 1/32 (8 − i)/8 i 1 to 6 Besides refining the structure of blockchain, Ethereum has another two security improvements. One is the random tie- breaking rule. Instead of accepting the first chain received , a node randomly selects one chain within all received chains of the same length. The number γ is not fixed anymore and equals the inverse of the competing chains number. The other one aims to remit the uncle mining strategy . Uncle blocks and eclipse attacks How do eclipse attacks work? An eclipse attack requires the attacker to control a of host nodes (each with their own IP address) and work out the neighboring nodes of a victim, essentially by trial and error. The effort required to achieve this depends on the size and nature of a network, but when successful, the attacker will be in control of all the victim’s connecting nodes after the victim logs off and rejoins the network. botnet In this scenario, there are three actors-the attacker, the victim, and an honest node. If the victim finds a block, an attacker does not propagate this block to the network. If the attacker finds a block, they share it with the victim. In this way, the attacker creates a private blockchain for themselves and the eclipsed node. By doing this, the attacker can obtain up to 96.4% more than with honest mining. Eclipse attacks are possible because in a decentralized network, a node cannot simultaneously connect to all other nodes on the network. Instead, for efficiency, a given node will connect to a select group of others, who in turn are connected to a select group of their own. So, it is possible to launch an eclipse attacks with only two machines with unique IP addresses. Ethereum relies on a structured network based on a protocol called , which is designed to allow nodes to connect to other nodes more efficiently. By using a key generation algorithm, an attacker could create an unlimited number of node IDs (identifiers on the peer-to-peer network) very quickly. Worse, an attacker could even create node IDs in a way that made them more attractive to the victim than a random node ID, basically drawing the victim to them. Kademlia Ethereum has three characteristics that could make it susceptible to eclipse attacks. Firstly, fast block creation creates endless loose blocks, indirectly infringing upon honest miners’ interests by inflating the supply of Ether. Secondly, the uncle block mechanism means that nodes can profit from these blocks. And thirdly, Ethereum’s enhanced node connectivity supplies a motivation to attackers. Also published on: https://cryptofanalex.medium.com/

Eclipse Attacks and Ethereum’s Uncle Block Mechanism

On May 12, the Eclipse Foundation launched the sixth edition of the IoT Developer Survey. We encourage developers in the IoT open source ecosystem to add your voice to the to help the industry gain the broadest possible view of the state of IoT technologies in the context of commercial IoT! IoT Developer Survey This year’s edition of the survey will provide essential insights into gaining a better understanding of the IoT industry landscape, the requirements and priorities IoT developers are facing, and how these trends will impact businesses and shape strategic business opportunities for software vendors, manufacturers, service providers, original equipment manufacturers (OEMs), enterprises of all sizes and individual developers in the IoT open source ecosystem. The survey results also help the IoT community at the Eclipse Foundation better understand the top industry focus areas and priorities for future project releases. hardware For the first time, the survey introduces Edge Computing questions, which will provide a solid framework for how developers implement Edge Computing technologies into their IoT solutions. Contribute to shaping the IoT ecosystem! open source I encourage developers in the IoT open source ecosystem to add your voice to the to help the gain the broadest possible view of the state of IoT technologies in the context of commercial IoT! It takes less than 10 minutes to complete the survey. IoT Developer Survey industry Note: The survey is completely anonymous. The data collected is used solely for the purpose of this survey and will not be shared with any third parties. The email address shared in the survey will be used only for sending the survey results after the survey analysis is completed.

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