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

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Ohio's state parks are bracing for thousands of visitors who want to see the once-in-a-lifetime solar eclipse on April 8.

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Day will turn into night in North Texas with the arrival of a total solar eclipse this April.

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When a solar eclipse treks across the U.S. in early April, it won’t be completely visible in Chicago and Illinois. While you might be able to catch a glimpse of it from home, you won’t have to travel too far to see the eclipse in all its glory. Parts of southern Illinois and Indiana will ...

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On April 8, students will trade in the classroom for the lawn to take in a planetary phenomenon.

NWACC holding campus-wide festival for solar eclipse

Atlas Obscura's Ecliptic Festival 2024 in Little Rock, Arkansas, will blend music, science, art, and wonder for four days around the total solar eclipse on April 8.

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Neighboring cities, including Dayton and Indianapolis, will have a front-row seat to the total eclipse.

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A long totality combined with a populated route means event could be relatively easy to see

Solar eclipse 2024: When is it and how to watch | The Independent

Eclipse glasses are a necessity for viewing the Great American Solar Eclipse

Eclipse glasses a must have for the Great American Solar Eclipse

Eclipse AI

eclipseai

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 Then why are we pelted with remarks like ‘Ruby is outdated’ and ‘Rails is irrelevant today'?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

Crypto Astrology: Eclipses, Hacks, and Blood Baths

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.

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