Telescopeis in the Customer Success Management Industry

Telescope

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The James Webb Space Telescope discovered a so-called &quot;dead&quot; galaxy that is the oldest known of its kind, having stopped its star formation more than 13 billion years ago.

Oldest 'dead' galaxy ever seen defies current models of the ancient universe | Space

Explore Space Telescope Live for official, up-to-date information on current, past, and upcoming investigations by NASA’s James Webb Space Telescope.

What Is Webb Observing Now? | Webb

How it Works by Archibald Williams is part of the HackerNoon Books Series. You can jump to any chapter in this book . THE MICROSCOPE, THE TELESCOPE, AND THE MAGIC-LANTERN. here Chapter XIII. THE MICROSCOPE, THE TELESCOPE, AND THE MAGIC-LANTERN. The simple microscope—Use of the simple microscope in the telescope—The terrestrial telescope—The Galilean telescope—The prismatic telescope—The reflecting telescope—The parabolic mirror—The compound microscope—The magic-lantern—The bioscope—The plane mirror. IN Fig. 119 is represented an eye looking at a vase, three inches high, situated at a, a foot away. If we were to place another vase, b, six inches high, at a distance of two feet; or c, nine inches high, at three feet; or d, a foot high, at four feet, the image on the retina would in every case be of the same size as that cast by a. We can therefore lay down the rule that . the apparent size of an object depends on the angle that it subtends at the eye To see a thing more plainly, we go nearer to it; and if it be very small, we hold it close to the eye. There is, however, a limit to the nearness to which it can be brought with advantage. The normal eye is unable to adapt its focus to an object less than about ten inches away, termed the "least distance of distinct vision." THE SIMPLE MICROSCOPE. A magnifying glass comes in useful when we want to examine an object very closely. The glass is a lens of short focus, held at a distance somewhat less than its principal focal length, f ( ), from the object. The rays from the head and tip of the pin which enter the eye are denoted by continuous lines. As they are deflected by the glass the eye gets the that a much longer pin is situated a considerable distance behind the real object in the plane in which the refracted rays would meet if produced backwards (shown by the dotted lines). The effect of the glass, practically, is to remove it (the object) to beyond the least distance of distinct vision, and at the same time to retain undiminished the angle it subtends at the eye, or, what amounts to the same thing, the actual size of the image formed on the retina. It follows, therefore, that if a lens be of such short focus that it allows us to see an object clearly at a distance of two inches—that is, one-fifth of the least distance of distinct vision—we shall get an image on the retina five times larger in diameter than would be possible without the lens. see Fig. 120 impression The two simple diagrams (Figs. 121 and 122) show why the image to be magnified should be nearer to the lens than the principal focus, f. We have already seen (Fig. 109) that rays coming from a point in the principal focal plane emerge as a parallel pencil. These the eye can bring to a focus, because it normally has a curvature for focussing parallel rays. But, owing to the power of "accommodation," it can also focus rays (Fig. 121), the eye lens thickening the necessary amount, and we therefore put our magnifying glass a bit nearer than f to get full advantage of proximity. If we had the object the principal focus, as in Fig. 122, the rays from it would converge, and these could not be gathered to a sharp point by the eye lens, as it cannot more than is required for focussing parallel rays. diverging outside flatten USE OF THE SIMPLE MICROSCOPE IN THE TELESCOPE. Let us now turn to Fig. 123. At a is a distant object, say, a hundred yards away. b is a double convex lens, which has a focal length of twenty inches. We may suppose that it is a lens in a camera. An inverted image of the object is cast by the lens at c. If the eye were placed at c, it would distinguish nothing. But if withdrawn to d, the least distance of distinct vision, behind c, the image is seen clearly. That the image really is at c is proved by letting down the focussing screen, which at once catches it. Now, as the focus of the lens is twice , the image will be twice as large as the object would appear if viewed directly without the lens. We may put this into a very simple formula:— d In Fig. 124 we have interposed between the eye and the object a small magnifying glass of 2½-inch focus, so that the eye can now clearly see the image when one-quarter away from it. b already magnifies the image twice; the eye-piece again magnifies it four times; so that the total magnification is 2 × 4 = 8 times. This result is arrived at quickly by dividing the focus of b (which corresponds to the object-glass of a telescope) by the focus of the eye-piece, thus:— d The ordinary astronomical telescope has a very long focus object-glass at one end of the tube, and a very short focus eye-piece at the other. To see an object clearly one merely has to push in or pull out the eye-piece until its focus exactly corresponds with that of the object-glass. THE TERRESTRIAL TELESCOPE. An astronomical telescope inverts images. This inversion is inconvenient for other purposes. So the terrestrial telescope (such as is commonly used by sailors) has an eye-piece compounded of four convex lenses which erect as well as magnify the image. Fig. 125 shows the simplest form of compound erecting eye-piece. THE GALILEAN TELESCOPE. A third form of telescope is that invented by the great Italian astronomer, Galileo, in 1609. Its principle is shown in Fig. 126. The rays transmitted by the object-glass are caught, coming to a focus, on a concave lens which separates them so that they appear to meet in the paths of convergence denoted by the dotted lines. The image is erect. Opera-glasses are constructed on the Galilean principle. before THE PRISMATIC TELESCOPE. In order to be able to use a long-focus object-glass without a long focussing-tube, a system of glass reflecting prisms is sometimes employed, as in Fig. 127. A ray passing through the object-glass is reflected from one posterior surface of prism a on to the other posterior surface, and by it out through the front on to a second prism arranged at right angles to it, which passes the ray on to the compound eye-piece. The distance between object-glass and eye-piece is thus practically trebled. The best-known prismatic telescopes are the Zeiss field-glasses. THE REFLECTING TELESCOPE. We must not omit reference to the telescope, so largely used by astronomers. The front end of the telescope is open, there being no object-glass. Rays from the object fall on a parabolic mirror situated in the rear end of the tube. This reflects them forwards to a focus. In the Newtonian reflector a plane mirror or prism is situated in the axis of the tube, at the focus, to reflect the rays through an eye-piece projecting through the side of the tube. Herschel's form of reflector has the mirror set at an angle to the axis, so that the rays are reflected direct into an eye-piece pointing through the side of the tube towards the mirror. reflecting THE PARABOLIC MIRROR. This mirror (Fig. 128) is of such a shape that all rays parallel to the axis are reflected to a common point. In the marine searchlight a powerful arc lamp is arranged with the arc at the focus of a parabolic reflector, which sends all reflected light forward in a pencil of parallel rays. The most powerful searchlight in existence gives a light equal to that of 350 million candles. THE COMPOUND MICROSCOPE. We have already observed (Fig. 110) that the nearer an object approaches a lens the further off behind it is the real image formed, until the object has reached the focal distance, when no image at all is cast, as it is an infinite distance behind the lens. We will assume that a certain lens has a focus of six inches. We place a lighted candle four feet in front of it, and find that a diminished image is cast on a ground-glass screen held seven inches behind it. If we now exchange the positions of the candle and the screen, we shall get an enlarged image of the candle. This is a simple demonstration of the law of —namely, that the distance between the lens and an object on one side and that between the lens and the corresponding image on the other bear a definite relation to each other; and an object placed at either focus will cast an image at the other. Whether the image is larger or smaller than the object depends on which focus it occupies. In the case of the object-glass of a telescope the image was at what we may call the focus. sharp conjugate foci short Now, a compound microscope is practically a telescope with the object at the focus, very close to a short-focus lens. A greatly enlarged image is thrown ( ) at the conjugate focus, and this is caught and still further magnified by the eye-piece. We may add that the object-glass, or , of a microscope is usually compounded of several lenses, as is also the eye-piece. long see Fig. 129 objective THE MAGIC-LANTERN. The most essential features of a magic-lantern are:—(1) The ; (2) the for concentrating the light rays on to the slide; (3) the for projecting a magnified image on to a screen. source of light condenser lens Fig. 130 shows these diagrammatically. The is most commonly an oil-lamp, or an acetylene gas jet, or a cylinder of lime heated to intense luminosity by an oxy-hydrogen flame. The natural combustion of hydrogen is attended by a great heat, and when the supply of oxygen is artificially increased the temperature of the flame rises enormously. The nozzle of an oxy-hydrogen jet has an interior pipe connected with the cylinder holding one gas, and an exterior, and somewhat larger, pipe leading from that containing the other, the two being arranged concentrically at the nozzle. By means of valves the proportions of the gases can be regulated to give the best results. illuminant The is set somewhat further from the illuminant than the principal focal length of the lenses, so that the rays falling on them are bent inwards, or to the slide. condenser The , or object lens, stands in front of the slide. Its position is adjustable by means of a rack and a draw-tube. The nearer it is brought to the slide the further away is the conjugate focus ( ), and consequently the image. The exhibitor first sets up his screen and lantern, and then finds the conjugate foci of slide and image by racking the lens in or out. objective see p. 239 If a very short focus objective be used, subjects of microscopic proportions can be projected on the screen enormously magnified. During the siege of Paris in 1870–71 the Parisians established a balloon and pigeon post to carry letters which had been copied in a minute size by photography. These copies could be enclosed in a quill and attached to a pigeon's wing. On receipt, the copies were placed in a special lantern and thrown as large writing on the screen. Micro-photography has since then made great strides, and is now widely used for scientific purposes, one of the most important being the study of the crystalline formations of metals under different conditions. THE BIOSCOPE. "Living pictures" are the most recent improvement in magic-lantern entertainments. The negatives from which the lantern films are printed are made by passing a ribbon of sensitized celluloid through a special form of camera, which feeds the ribbon past the lens in a series of jerks, an exposure being made automatically by a revolving shutter during each rest. The positive film is placed in a lantern, and the intermittent movement is repeated; but now the source of illumination is behind the film, and light passes outwards through the shutter to the screen. In the Urban bioscope the film travels at the rate of fifteen miles an hour, upwards of one hundred exposures being made every second. The impression of continuous movement arises from the fact that the eye cannot get rid of a visual impression in less than one-tenth of a second. So that if a series of impressions follow one another more rapidly than the eye can rid itself of them the impressions will overlap, and give one of , if the position of some of the objects, or parts of the objects, varies slightly in each succeeding picture. motion THE PLANE MIRROR. This chapter may conclude with a glance at the common looking-glass. Why do we see a reflection in it? The answer is given graphically by Fig. 131. Two rays, a , a , from a point a strike the mirror m at the points and . Lines n, o, drawn from these points perpendicular to the mirror are called their . The angles a n, a o are the of rays a , a . The paths which the rays take after reflection must make angles with n and o respectively equal to a n, a o. These are the . If the eye is so situated that the rays enter it as in our illustration, an image of the point a is seen at the point a1, in which the lines d , e meet when produced backwards. b c b c b c normals b c angles of incidence b c b c b c angles of reflection b c When the vertical mirror is replaced by a horizontal reflecting surface, such as a pond (Fig. 132), the same thing happens. The point at which the ray from the reflection of the spire's tip to the eye appears to pass through the surface of the water must be so situated that if a line were drawn perpendicular to it from the surface the angles made by lines drawn from the real spire tip and from the observer's eye to the base of the perpendicular would be equal. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Archibald Williams (2009). How it Works. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/28553/pg28553-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

THE MICROSCOPE, THE TELESCOPE, AND THE MAGIC-LANTERN.

Nowadays, an essential part of the development process is to track the performance of an application. Analyzing the processes in the system allows you to better understand which part of the project is working well, and where urgent fixes are needed. Among the many tools that allow you to monitor and debug, there is one that is very easy to customize in the Laravel framework, because it is made by the creators of the framework themselves. Its name is Telescope. Laravel Telescope is a debugging panel, with a user-friendly and modern design. Inside the panel you can find logs and statuses of all elements of the system. Features Laravel Telescope is a customized debugging panel with different aspects of the application, where each aspect is highlighted in a separate menu item. Telescope allows you to see all parts of the system, such as database accesses, cache accesses, how to work with built-in events, how to work with custom events. Also there is realized the display of requests to the system or errors. We will talk more about the features a little later, but for now let's set up the application. Local and production installation The main convenience of working with Telescope is that it can be configured literally in a couple of clicks. After installing the package you need to load the assets with the command telescope:install , and then run migrations. When installing only locally we need, after adding the package to dev dependencies and loading the assets, to remove the TelescopeServiceProvider connection from config/app.php, but set the provider connection depending on the environment in AppServiceProvider, for example as follows if ($this->app->environment('local')) {
   $this->app->register(\Laravel\Telescope\TelescopeServiceProvider::class);
   $this->app->register(TelescopeServiceProvider::class);
} After that you need to remove the Telescope package from discover in composer.json.
As for the production solution, there can't be one opinion. On the one hand - when installing and on production environment there is no need to finalize the connection, because in this case it will be available everywhere. It is also a plus, as it gives you the opportunity to quickly see what is happening with the state of production.
However, such a solution has a disadvantage and quite a big one.
Due to the fact that the data that is shown in Telescope is extremely sensitive, it is necessary to correctly configure the routing and disable it from being available without access to the corporate network.
In my opinion, the ideal connection would be to access a local Laravel Telescope that accesses all data sources within the corporate network.
The last step in setting up this tool is to set up the configuration in config/telescope.php Let's see how it works By default Telescope runs at /telescope. With a fresh installation, you probably won't see any requests or other data. But what we can say with certainty is that there is a lot of possibilities for this tool, if you look even just at the list of menus. As for the settings for connecting different services, we will talk about it below. In the meantime, it is still necessary to understand how to set up the panel for yourself. Prune command The first problem in our application is the problem with data deletion. Since Telescope collects all the data it sees, the database often quickly reaches its limit without deleting old records. Because of this, we need to update the database by force. Every application has a different frequency of data refresh, but if we're talking about a small project, we can do it on a daily basis. This can be achieved by running the artisan command telescope:prune, or by setting it automatically in Console\Kernel. Accessing the Telescope panel By default, working with the panel is only allowed locally. To fix this, you need to add some modified logic to the Gate check in TelescopeServiceProvider and specify there, for example, your email address. protected function gate(): void
{
   Gate::define('viewTelescope', function ($user) {
       return in_array($user->email, [
           'example@gmail.com'
       ]);
   });
} As you can see in the listing - adding a rule for validation is not a difficult task. Filters From time to time, when working with Telescope, especially in production, there is a problem with disk space. In order to avoid this and not to disable all logs so often, Laravel Telescope provides filters. They allow us to record only important parts, such as errors or dropped jobs. To change this we need to add or remove necessary/unnecessary parts in TelescopeServiceProvider, for example like this: Telescope::filter(function (IncomingEntry $entry) {
   if ($this->app->environment('local')) {
       return true;
   }


   return $entry->isReportableException() ||
          $entry->isFailedRequest() ||
          $entry->isFailedJob() ||
          $entry->hasMonitoredTag();
}); So the filters helped to limit the set of logs when working on production. Tags If you look at the previous listing, there is a method hasMonitoredTag(). It allows you to show all records that have a tag set, regardless of which watcher it belongs to. In order to set a tag on, for example, a query, you will need to add the following to the TelescopeServiceProvider: Telescope::tag(function (IncomingEntry $entry) {
   return $entry->type === 'request'
       ? ['status:'.$entry->content['response_status']]
       : [];
}); All tracked tags can be viewed by clicking on the button in the top right corner of the admin panel interface. Available watchers Finally, we have observed all the formalities and come to the most interesting part. Laravel Telescope contains a huge number of integrations with different services. Let's take a look at the most popular watchers. Adding a watcher to the system is done in the config/telescope configuration file. If you need to add login data to a new watcher, the new data is written directly to the 'watchers' key. The full list is quite large and while some work with obvious entities like CacheWatcher, CommandWatcher, RequestWatcher, others need a bit more detail. BatchWatcher - this watcher keeps track of sending jobs in batches. Read more about this method here EventWatcher and ModelWatcher - it's worth noting that EventWatcher does not track changes to models that use standard model events, unlike ModelWatcher GateWatcher - records all checks of gates and policies and their results. Below, for information, are examples of how some of the pages work. CacheWatcher Tracks cache hits, including unsuccessful ones. You can also see the passed arguments and options DumpWatcher Shows all calls to dump() function It is worth noting that this watcher does not work when using as a cache driver. array EventWatcher Shows all custom events that have occurred recently. You can also track the listener that worked on a particular event. ExceptionWatcher One of the most useful watchers, as it allows you to see errors, including the code where the error is thrown, as well as stacktrace Upgrade Telescope version It cannot be ignored that upgrading the Telescope version can cause some difficulties. To solve them, you need to follow all the steps of the guide on the official website, and make a couple of points. Due to the fact that the published assets may change during the upgrade, you need to upload them again during the upgrade, either manually or by setting post-update-cmd in composer.json Conclusion There are a huge number of all sorts of options on the market right now for debugging and tracking the state of a project. Laravel Telescope may not be very suitable for Production solutions, but for local development the tool is almost indispensable. It is worth noting that Laravel Telescope is updated quite often, which gives hope for long term support.

Companion for working with Laravel — Telescope

Navigating the Enigmatic Labyrinth of Web3 Gaming: A Trilogy of Riveting Challenges Welcome to the enigmatic labyrinth of Web3 gaming, an exhilarating new realm teeming with uncharted territories, unforeseen challenges, and unexplored potential. Amidst its intoxicating intrigue, we grapple with three colossal challenges that demand our immediate attention: forging sustainable virtual economies, deciphering the enigma of player behavior, and harnessing the power of advanced data analysis tools. So buckle up and let's embark on this riveting odyssey. I. Virtual Economies: The High-Stakes Chess of Balancing Supply and Demand Picture yourself as the sovereign of an enigmatic, pulsating metropolis. Your mission: ensure prosperity flourishes, balance prevails, and citizens revel in their existence. Intimidating, isn't it? Now, transpose this scenario onto an expansive, kinetic gaming universe and you'll fathom the bewitching complexity of governing virtual economies. Just like their tangible counterparts, in-game economies thrive on equilibrium. Too much abundance leads to debilitating hyperinflation, rendering those hard-earned, coveted in-game trophies mere trinkets. On the flip side, extreme scarcity catapults us into a vortex of deflation, where even mundane items demand a king's ransom. The objective: conjure a harmonious sweet spot between these extremes - a precarious endeavor akin to ballet dancing on a razor's edge, but ecstatically exhilarating when mastered. The stakes are high. A striking revelation from a in 2020 disclosed players shelled out a whopping $64.4 billion on in-game purchases, reinforcing the gargantuan significance of virtual economies in games. SuperData Research study II. Decoding Player Behavior: The Da Vinci Code of Gaming Every player is a unique, enigmatic conundrum. Some are intrepid explorers, some tactical maestros, while others thrive on anarchy. Unraveling these multifarious behaviors is akin to deciphering the Da Vinci code - baffling, hair-pulling, yet incredibly gratifying when you crack it. The era when game developers could release games with blithe disregard for player sentiment is a relic of the past. Player behavior analysis now occupies the throne. Reinforcing this reality, an Entertainment Software Association survey revealed a : 64% of adults and 70% of minors in the U.S. are gamers, underlining the necessity to cater to a diverse symphony of player behaviors. riveting statistic III. Game Data Analysis: The Oracle's Vision in Decision-Making Visualize game data analysis as an oracle's crystal ball. It foretells the future (or a close facsimile thereof), furnishes critical insights, and steers decision-making. Within the gaming cosmos, such a tool empowers developers to fine-tune game economies, refine gameplay mechanics, and comprehend why players bid their game adieu. Traditional tools often falter, overwhelmed by the colossal avalanche of data spawned by games or failing to deliver actionable insights. With DFC Intelligence projecting the global video game market to catapult to , potent data analysis transcends being a luxury and morphs into a veritable imperative. $250 billion by 2025 In summary, these imposing challenges, albeit intimidating, represent an exhilarating opportunity to redefine the world of Web3 gaming. Like a captivating puzzle, they infuse this journey with an element of enigma and excitement that only adds to the allure of this dynamic frontier! Stepping into the Spotlight: How Telescope Labs Aims to Game-Change the Web3 Landscape Picture this: you're standing on the edge of an unexplored gaming universe, teeming with exciting quests, hidden treasures, and thrilling challenges. But wait! You're not alone. Right beside you, wielding an arsenal of AI-powered tools, stands a formidable ally - . Telescope Labs : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program From its humble beginnings in London, is on a daring mission to solve the riddles we just spoke about. So, hold onto your gaming controllers as we embark on this roller coaster ride! Telescope Labs Battling Virtual Hyperinflation and Deflation with a Secret Weapon Remember how we discussed the wild fluctuations in in-game economies that can send players packing? Well, Telescope Labs has found a magic potion that could stabilize these virtual economies and maintain harmony. Their secret weapon? Groundbreaking Market Intelligence and Data Analytic tools! This dynamic duo brings harmony to chaotic game economies, ensuring neither hyperinflation nor deflation spoils your virtual shopping spree. It's as if they've unlocked the cheat code to a balanced game economy. Deciphering the Enigma of Player Behavior To tackle the Rubik's cube of diverse player behaviors, Telescope Labs has brought in a special guest: artificial intelligence. Leveraging AI, the company has crafted a unique lens that offers a deep understanding of the myriad player behaviors. It's like having a secret playbook that reveals the thoughts, preferences, and playstyles of gamers. Suddenly, the Rubik's cube doesn't seem so daunting, does it? Unleashing the Crystal Ball of Game Data Analysis The crystal ball we fantasized about? It's real! And it goes by the name of Vantage. Telescope Labs' crown jewel, Vantage, is a marvel in the realm of game data analysis and decision-making. This nifty tool is like the Sherlock Holmes of data analysis, adept at deciphering clues and revealing insights that can transform your game universe. It enables gaming companies to unlock the full potential of their game economies. What's even cooler? Vantage speaks human! With its natural language query interface, anyone, even a beginner, can converse with it. Crafting Partnerships that Forge New Frontiers Telescope Labs is not going solo on this epic quest. They've formed alliances with industry titans like Gamevolution, PlayEmber, Widow Games, and Pink Moon Studios. It's as if the Avengers of the gaming world have assembled to revolutionize the Web3 landscape! As the gaming world stands on the brink of a new era, Telescope Labs is poised to steer it towards a future teeming with sustainability, understanding, and innovation. So, gamers and developers alike, buckle up, because with Telescope Labs, we're all in for a gaming revolution that's set to transcend Web 2.0 and 3.0! In the immortal words of ' CEO, Semih Gilan, "We can't wait to embark on this exciting journey together with our partners and clients. By unlocking the power of LLMs, we pioneer evolution on game analytics and empower developers to revolutionize the industry." Telescope Labs And revolutionize, they will! Ready to join this thrilling adventure with Telescope Labs? Game on! Don’t forget to like and share the article!

Web3 Gaming Redefined by Telescope Labs: Master the New Frontier

Juegos Web3 redefinidos por Telescope Labs: domina la nueva frontera

Web3 Gaming redéfini par Telescope Labs : maîtrisez la nouvelle frontière

टेलीस्कोप लैब्स द्वारा वेब3 गेमिंग को पुनर्परिभाषित: मास्टर द न्यू फ्रंटियर

Telescope Labs によって再定義された Web3 ゲーム: 新しいフロンティアをマスターする

Web3 Gaming Redefinido pelo Telescope Labs: Domine a Nova Fronteira

Trò chơi Web3 được Telescope Labs định nghĩa lại: Làm chủ biên giới mới

Telescope Labs 重新定义 Web3 游戏：掌握新领域

Pleasures of the telescope by Garrett Putman Serviss, is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : Pleasures of the telescope Title : Garrett Putman Serviss Author : May 10, 2009 [eBook #28752] Release date : English Language Table of Links CHAPTER 1 CHAPTER 2 CHAPTER 3 CHAPTER 4 CHAPTER 5 CHAPTER 6 CHAPTER 7 CHAPTER 8 CHAPTER 9 CHAPTER 1 About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Garrett Putman Serviss (2009). Pleasures of the telescope. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/28752/pg28752-images.html . This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at .html https://www.gutenberg.org/policy/license

Pleasures of the telescope by Garrett Putman Serviss - Table of Links

is a technology that corrects the distortion in an image caused by natural effects such as air turbulence and change in the local temperature and atmosphere. Adaptive Optics The agile development of professional astronomy, plus the growing awareness regarding the benefits of using lightweight materials in manufacturing, are expected to offer positive momentum to the adaptive optics component market during the forecast period, 2019–2026. Telescope technology is evolving and is close to the next generation of discoveries, which offer an opportunity for the introduction and use of lightweight materials such as carbon fiber-reinforced polymer (CFRP) in the manufacturing of telescopes and optics. Segmental Insights The has been segmented on the basis of type, application, and region. global adaptive optics components market , the adaptive optics components market has been segmented into wavefront sensors, wavefront modulators, control systems, and others. On the basis of type The wavefront sensor segment is projected to register an increase in demand, in part due to the growth of the biomedical sector. This has also contributed to increasing the application of wavefront sensors in ophthalmology in recent years. By application, the adaptive optics components market has been segmented into consumer goods, astronomy, military & defense, biomedical, and others. Recently, the use of adaptive optics has become more popular in astronomy to improve image resolution, which is expected to fuel the growth of the adaptive optics component market in the segment. Regional Analysis Geographically, the global adaptive optics component market has been segmented into North America, Europe, Asia Pacific, and Latin America, Middle East and Africa (LAMEA). North America is expected to witness significant growth, owing to the increasing focus on the development of advanced optics systems and their adoption in the astronomical field for the delivery of sharper images. Adequate support from government agencies in terms of funding for the development of advanced telescopes and astronomical equipment is also pegged to be a key factor favoring the market growth. For instance, in , which would help astronomers study the outer reaches of the known universe. April 2015, the Canadian Federal Government announced funding of approximately USD 234.5 million for the development of a 30-meter telescope Europe is expected to witness considerable growth in the adaptive optics components market during the forecast period, 2019–2026. This can be attributed to the key role of technology in the success of the European Extremely Large Telescope (E-ELT) project. Asia Pacific is anticipated to witness substantial growth in the adaptive optics technology market. It should be noted though that while adaptive optics significantly improve image quality, the high cost of the overall system is a factor that restricts the market growth in the region. The LAMEA region is expected to witness moderate growth, due to the integration of high-end adaptive optics in military vehicles and equipment for the enhancement of surveillance efficiency. Key Players Some of the major players identified in the adaptive optics component market are Northrop Grumman Corporation (U.S.), Iris AO, Inc. (U.S.), HOLOEYE Photonics AG (Germany), A.D.S. International S.r.l. (Italy) and Canon Inc. (Japan). Segmentation By Type Wavefront sensors Wavefront Modulators Control Systems Others By Application Consumer Goods Astronomy Military & Defense Biomedical Others By Region North America Europe Asia Pacific LAMEA Previously published here.

How Will the Adaptive Optics and Telescope Industries Help Each Other Evolve? 

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1974, Ap&SS, 28, 45, doi: 10.1007/BF00642237 Bondi, H. 1952, MNRAS, 112, 195, doi: 10.1093/mnras/112.2.195 Booth, C. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1974, Ap&SS, 28, 45, doi: 10.1007/BF00642237 Bondi, H. 1952, MNRAS, 112, 195, doi: 10.1093/mnras/112.2.195 Booth, C. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

All the References That We Used in Our Black Hole Study So You Can Continue to Learn

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Discussion

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Flare Analyses

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University;
(2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics;
(3) Nicole Arulanantham, Space Telescope Science Institute;
(4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro
X-ray Observations and Data Extraction
Flare Analyses
Comparison with X-ray Flares from Young Stars
Discussion
Conclusions
Acknowledgments and References 6. CONCLUSIONS Drawing upon recent observations conducted during a single orbit of DQ Tau in July-August 2022, which utilized NuSTAR, Swift, and Chandra telescopes (§ 2), alongside previously gathered X-ray and mm-band data from multiple periastrons of DQ Tau (Salter et al. 2010; Getman et al. 2011, 2022b), our study embarks on an extensive analysis to compute the energetic characteristics of X-ray/NUV/optical flares within DQ Tau (§ 3). Serendipitously, we discovered X-ray super-flares outside of periastron, potentially related to interacting magnetospheres (§ 5.1.2). The absence of evidence for long-term variability in the baseline X-ray emission of ∼ 1 Myr old DQ Tau is consistent with the understanding that younger stars typically exhibit larger active regions and more extensive X-ray coronal structures. This may contribute to the reduction of observable magnetic dynamo cycling (§ 5.3). This paper is available on arxiv under CC 4.0 license. Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro X-ray Observations and Data Extraction Flare Analyses Comparison with X-ray Flares from Young Stars Discussion Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro X-ray Observations and Data Extraction X-ray Observations and Data Extraction Flare Analyses Flare Analyses Comparison with X-ray Flares from Young Stars Comparison with X-ray Flares from Young Stars Discussion Discussion Conclusions Conclusions Acknowledgments and References Acknowledgments and References 6. CONCLUSIONS Drawing upon recent observations conducted during a single orbit of DQ Tau in July-August 2022, which utilized NuSTAR, Swift, and Chandra telescopes (§ 2), alongside previously gathered X-ray and mm-band data from multiple periastrons of DQ Tau (Salter et al. 2010; Getman et al. 2011, 2022b), our study embarks on an extensive analysis to compute the energetic characteristics of X-ray/NUV/optical flares within DQ Tau (§ 3). Serendipitously, we discovered X-ray super-flares outside of periastron, potentially related to interacting magnetospheres (§ 5.1.2). The absence of evidence for long-term variability in the baseline X-ray emission of ∼ 1 Myr old DQ Tau is consistent with the understanding that younger stars typically exhibit larger active regions and more extensive X-ray coronal structures. This may contribute to the reduction of observable magnetic dynamo cycling (§ 5.3).

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Conclusions

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University;
(2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics;
(3) Nicole Arulanantham, Space Telescope Science Institute;
(4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro
X-ray Observations and Data Extraction
Flare Analyses
Comparison with X-ray Flares from Young Stars
Discussion
Conclusions
Acknowledgments and References 4. COMPARISON WITH X-RAY FLARES FROM YOUNG STARS We conducted three observations of the periastron passages of DQ Tau using X-ray telescopes: in 2010 (Getman et al. 2011), 2021 (Getman et al. 2022a), and 2022 (current study). Notably, significant X-ray flares were detected on all three occasions. In Figure 8, we compare the duration, peak X-ray luminosity, and energetics of these DQ Tau flares with numerous large X-ray flares produced by young stars, as studied by Getman et al. (2008, also known as COUP flares) and Getman & Feigelson (2021, also known as MYStIX/SFiNCs flares). In Figure 8, we only include the “main” DQ Tau X-ray flares that occur roughly within the orbital phase range of (0.95 − 1.05). However, it is important to note that more X-ray flaring events of comparable energetics are present within the (1.05 − 1.2) orbital phase range (see Figure 2 here and Figure 1 in Getman et al. (2022a)). It is also noteworthy that somewhat different methodologies for flare detection and analysis were employed in Getman et al. (2008) and Getman & Feigelson (2021), due to distinct scientific objectives pursued in these two papers As a result, Getman et al. (2008) reported the rise and decay timescales, flare peak X-ray luminosities, but no energies for the COUP flares, while Getman & Feigelson (2021) reported only total flare durations (with no differentiation between rise and decay), flare peak Xray luminosities, and energies for the MYStIX/SFiNCs flares. Consequently, the rise and decay timescales, as well as flare peak X-ray luminosities, are compared between the DQ Tau and COUP flares (see Figure 8a and b), while the flare durations, peak luminosities, and energies are compared between the DQ Tau and MYStIX/SFiNCs flares (see Figure 8c and d). Figure 8 illustrates that the DQ Tau flares lie within the loci of the COUP and MYStIX/SFiNCs flares, albeit having relatively long durations and relatively low peak flare X-ray luminosities. This places them at the sensitivity limit border of the COUP and MYStIX/SFiNCs flare surveys. This paper is available on arxiv under CC 4.0 license. Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro X-ray Observations and Data Extraction Flare Analyses Comparison with X-ray Flares from Young Stars Discussion Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro X-ray Observations and Data Extraction X-ray Observations and Data Extraction Flare Analyses Flare Analyses Comparison with X-ray Flares from Young Stars Comparison with X-ray Flares from Young Stars Discussion Discussion Conclusions Conclusions Acknowledgments and References Acknowledgments and References 4. COMPARISON WITH X-RAY FLARES FROM YOUNG STARS We conducted three observations of the periastron passages of DQ Tau using X-ray telescopes: in 2010 (Getman et al. 2011), 2021 (Getman et al. 2022a), and 2022 (current study). Notably, significant X-ray flares were detected on all three occasions. In Figure 8, we compare the duration, peak X-ray luminosity, and energetics of these DQ Tau flares with numerous large X-ray flares produced by young stars, as studied by Getman et al. (2008, also known as COUP flares) and Getman & Feigelson (2021, also known as MYStIX/SFiNCs flares). In Figure 8, we only include the “main” DQ Tau X-ray flares that occur roughly within the orbital phase range of (0.95 − 1.05). However, it is important to note that more X-ray flaring events of comparable energetics are present within the (1.05 − 1.2) orbital phase range (see Figure 2 here and Figure 1 in Getman et al. (2022a)). It is also noteworthy that somewhat different methodologies for flare detection and analysis were employed in Getman et al. (2008) and Getman & Feigelson (2021), due to distinct scientific objectives pursued in these two papers As a result, Getman et al. (2008) reported the rise and decay timescales, flare peak X-ray luminosities, but no energies for the COUP flares, while Getman & Feigelson (2021) reported only total flare durations (with no differentiation between rise and decay), flare peak Xray luminosities, and energies for the MYStIX/SFiNCs flares. Consequently, the rise and decay timescales, as well as flare peak X-ray luminosities, are compared between the DQ Tau and COUP flares (see Figure 8a and b), while the flare durations, peak luminosities, and energies are compared between the DQ Tau and MYStIX/SFiNCs flares (see Figure 8c and d). Figure 8 illustrates that the DQ Tau flares lie within the loci of the COUP and MYStIX/SFiNCs flares, albeit having relatively long durations and relatively low peak flare X-ray luminosities. This places them at the sensitivity limit border of the COUP and MYStIX/SFiNCs flare surveys.

Colliding Magnetospheres: Comparison with X-ray Flares from Young Stars

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau:Acknowledgments and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University;
(2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics;
(3) Nicole Arulanantham, Space Telescope Science Institute;
(4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro
X-ray Observations and Data Extraction
Flare Analyses
Comparison with X-ray Flares from Young Stars
Discussion
Conclusions
Acknowledgments and References 2. X-RAY OBSERVATIONS AND DATA EXTRACTION 2.1. NuSTAR Data We conducted a single observation (ObsID 30801011002; PI K. Getman) of this system with NuSTAR (Harrison et al. 2013) from 04:51:09 UTC on July 28, 2022, to 19:36:09 UTC on July 31, 2022. This time window covers the orbital phase range of Φ = (0.9 − 1.13) around the periastron passage on July 30, 2022 (Φ = 1.0). The science exposure of the observation, taking into account Earth occultations, is 156 ksec. The data obtained from focal plane modules A and B (FPMA and FPMB), covering similar energy ranges, were processed using tools from the NuSTAR Data Analysis Software package NuSTARDAS (v. 2.1.2), which is incorporated into HEASOFT (v. 6.31.1) as detailed in (Nasa High Energy Astrophysics Science Archive Research Center (Heasarc) 2014). NuSTAR’s calibration database (CALDB) v. 20221229 was utilized. The data went through calibration and screening using the nupipeline tool. Parameters saamode=OPTIMIZED and tentacle=yes were employed to screen the data for elevated count rates resulting from the spacecraft’s passages through the South Atlantic Anomaly. Subsequently, the nuproducts tool was utilized to generate various outputs, including the source and background lightcurves and spectra, as well as response and response matrix files. Figures 1(a,b) present images of the combined FPMA and FPMB event lists for the (3 − 10) keV and (10 − 50) keV energy bands, respectively. The source counts were obtained from a circular region with a radius of 40′′ (indicated by the green circle in Figure 1), representing 60% of the energy within the point spread function. The background measurement was performed locally in an area devoid of sources. Within the source extraction circle, there are 771 X-ray events with energies ranging from 3 to 10 keV, approximately one third of which are background events. The source is not detected in the (10 − 20) keV band (not shown) or the (10 − 50) keV band (Figure 1b), nor at higher energies. 2.2. Swift Data Using the Neil Gehrels Swift observatory (Gehrels et al. 2004), we conducted 16 short observations of DQ Tau near periastron over a time period of July 28 to August 2, 2022. These observations are part of our joint NuSTAR/Swift program. The observations were spaced several hours apart, with durations ranging from 1 to 1.7 ksec, totaling 22.5 ksec. The target ID for these observations is 14857. The X-ray Telescope (XRT) operated in the PC mode, while the Ultraviolet/Optical Telescope (UVOT) operated in the 0x30ed standard sixfilter blue-weighted mode. The Swift-XRT data product generator (Evans et al. 2007, 2009) was utilized to construct X-ray light curves and source/background spectra, along with the relevant calibration files. The generator employed HEASOFT package (v. 6.29) and CALDB (v. 20230109). Figure 1c presents the XRT image obtained from merging the event lists of all 16 observations. Within the circular source extraction region of a 30′′ radius (indicated by the green circle in the image), we identified 249 X-ray counts with energies ranging from 0.2 to 10 keV, of which only a few percent represented background counts. For each of the 16 observations, UVOT magnitudes for the six filters (V:B:U:W1:M2:W2) were measured by applying the fappend (Blackburn et al. 1999) and uvotmaghist tools from HEASOFT (v. 6.31.1). 2.3. Chandra Data To investigate the soft X-ray emission throughout the entire orbital phase of DQ Tau and complement the observations made by NuSTAR and Swift at periastron, additional X-ray data were obtained utilizing the Chandra X-ray Observatory (Weisskopf et al. 2002). The investigation involved 12 short Chandra imaging observations of DQ Tau away from periastron, with each observation lasting approximately 1.5 ksec. These observations were part of the Director’s Discretionary Time (DDT) program, with corresponding observation IDs ranging from 26464 to 26475. Data were obtained between August 1 and August 14, 2022, covering an orbital phase range of 1.1 to 1.9. To mitigate potential pileup effects during anticipated Xray flares, a 1/8 sub-array of a single ACIS-I3 chip was employed (Garmire et al. 2003). For the Chandra data reduction and analysis, CIAO v4.15(Fruscione et al. 2006) and CALDB v4.10.4 were utilized. The CIAO tools chandra repro and reproject obs were used to reprocess the data and merge the event images. Figure 1d displays the cutout of the merged Chandra-ACIS-I3 image of DQ Tau. Count rates and apparent fluxes were measured, and spectra and response files were generated using the srcflux tool. Within the circular source extraction region, with a radius of 2′′ (indicated by the green circle in the image), and within the energy band of (0.5 − 8) keV, net X-ray events were observed between 30 and 50 per observation in 8 instances. However, the remaining 4 consecutive observations (ObsIDs 26471, 26472, 26473, and 26474) exhibited higher net count levels, ranging from 61 to 781 counts per observation. This paper is available on arxiv under CC 4.0 license. Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro X-ray Observations and Data Extraction Flare Analyses Comparison with X-ray Flares from Young Stars Discussion Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro X-ray Observations and Data Extraction X-ray Observations and Data Extraction Flare Analyses Flare Analyses Comparison with X-ray Flares from Young Stars Comparison with X-ray Flares from Young Stars Discussion Discussion Conclusions Conclusions Acknowledgments and References Acknowledgments and References 2. X-RAY OBSERVATIONS AND DATA EXTRACTION 2.1. NuSTAR Data We conducted a single observation (ObsID 30801011002; PI K. Getman) of this system with NuSTAR (Harrison et al. 2013) from 04:51:09 UTC on July 28, 2022, to 19:36:09 UTC on July 31, 2022. This time window covers the orbital phase range of Φ = (0.9 − 1.13) around the periastron passage on July 30, 2022 (Φ = 1.0). The science exposure of the observation, taking into account Earth occultations, is 156 ksec. The data obtained from focal plane modules A and B (FPMA and FPMB), covering similar energy ranges, were processed using tools from the NuSTAR Data Analysis Software package NuSTARDAS (v. 2.1.2), which is incorporated into HEASOFT (v. 6.31.1) as detailed in (Nasa High Energy Astrophysics Science Archive Research Center (Heasarc) 2014). NuSTAR’s calibration database (CALDB) v. 20221229 was utilized. The data went through calibration and screening using the nupipeline tool. Parameters saamode=OPTIMIZED and tentacle=yes were employed to screen the data for elevated count rates resulting from the spacecraft’s passages through the South Atlantic Anomaly. Subsequently, the nuproducts tool was utilized to generate various outputs, including the source and background lightcurves and spectra, as well as response and response matrix files. Figures 1(a,b) present images of the combined FPMA and FPMB event lists for the (3 − 10) keV and (10 − 50) keV energy bands, respectively. The source counts were obtained from a circular region with a radius of 40′′ (indicated by the green circle in Figure 1), representing 60% of the energy within the point spread function. The background measurement was performed locally in an area devoid of sources. Within the source extraction circle, there are 771 X-ray events with energies ranging from 3 to 10 keV, approximately one third of which are background events. The source is not detected in the (10 − 20) keV band (not shown) or the (10 − 50) keV band (Figure 1b), nor at higher energies. 2.2. Swift Data Using the Neil Gehrels Swift observatory (Gehrels et al. 2004), we conducted 16 short observations of DQ Tau near periastron over a time period of July 28 to August 2, 2022. These observations are part of our joint NuSTAR/Swift program. The observations were spaced several hours apart, with durations ranging from 1 to 1.7 ksec, totaling 22.5 ksec. The target ID for these observations is 14857. The X-ray Telescope (XRT) operated in the PC mode, while the Ultraviolet/Optical Telescope (UVOT) operated in the 0x30ed standard sixfilter blue-weighted mode. The Swift-XRT data product generator (Evans et al. 2007, 2009) was utilized to construct X-ray light curves and source/background spectra, along with the relevant calibration files. The generator employed HEASOFT package (v. 6.29) and CALDB (v. 20230109). Figure 1c presents the XRT image obtained from merging the event lists of all 16 observations. Within the circular source extraction region of a 30′′ radius (indicated by the green circle in the image), we identified 249 X-ray counts with energies ranging from 0.2 to 10 keV, of which only a few percent represented background counts. For each of the 16 observations, UVOT magnitudes for the six filters (V:B:U:W1:M2:W2) were measured by applying the fappend (Blackburn et al. 1999) and uvotmaghist tools from HEASOFT (v. 6.31.1). 2.3. Chandra Data To investigate the soft X-ray emission throughout the entire orbital phase of DQ Tau and complement the observations made by NuSTAR and Swift at periastron, additional X-ray data were obtained utilizing the Chandra X-ray Observatory (Weisskopf et al. 2002). The investigation involved 12 short Chandra imaging observations of DQ Tau away from periastron, with each observation lasting approximately 1.5 ksec. These observations were part of the Director’s Discretionary Time (DDT) program, with corresponding observation IDs ranging from 26464 to 26475. Data were obtained between August 1 and August 14, 2022, covering an orbital phase range of 1.1 to 1.9. To mitigate potential pileup effects during anticipated Xray flares, a 1/8 sub-array of a single ACIS-I3 chip was employed (Garmire et al. 2003). For the Chandra data reduction and analysis, CIAO v4.15(Fruscione et al. 2006) and CALDB v4.10.4 were utilized. The CIAO tools chandra repro and reproject obs were used to reprocess the data and merge the event images. Figure 1d displays the cutout of the merged Chandra-ACIS-I3 image of DQ Tau. Count rates and apparent fluxes were measured, and spectra and response files were generated using the srcflux tool. Within the circular source extraction region, with a radius of 2′′ (indicated by the green circle in the image), and within the energy band of (0.5 − 8) keV, net X-ray events were observed between 30 and 50 per observation in 8 instances. However, the remaining 4 consecutive observations (ObsIDs 26471, 26472, 26473, and 26474) exhibited higher net count levels, ranging from 61 to 781 counts per observation.

Colliding Magnetospheres: X-ray Observations and Data Extraction

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University;
(2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of
Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics;
(3) Nicole Arulanantham, Space Telescope Science Institute;
(4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro
X-ray Observations and Data Extraction
Flare Analyses
Comparison with X-ray Flares from Young Stars
Discussion
Conclusions
Acknowledgments and References ABSTRACT DQ Tau is a unique young high-eccentricity binary system that exhibits regular magnetic reconnection flares and pulsed accretion near periastron. We conducted NuSTAR, Swift, and Chandra observations during the July 30, 2022 periastron to characterize X-ray, near-ultraviolet (NUV), and optical flaring emissions. Our findings confirm the presence of X-ray super-flares accompanied by substantial NUV and optical flares, consistent with previous discoveries of periastron flares in 2010 and 2021. These observations, supported by new evidence, strongly establish the magnetosphere collision mechanism as the primary driver of magnetic energy release during DQ Tau’s periastron flares. The energetics of the observed X-ray super-flares remain consistent across the three periastrons, indicating recurring energy sources during each passage, surpassing the capabilities of single stars. The observed flaring across multiple bands supports the Adams et al. model for magnetosphere interaction in eccentric binaries. Evidence from modeling and past and current observations suggests that both the mm/X-ray periastron flares and tentatively, the magnetic reconnection-related components of the optical/NUV emissions, conform to the classical solar/stellar non-thermal thick-target model, except for the distinctive magnetic energy source. However, our NuSTAR observations suffered from high background levels, hindering the detection of anticipated non-thermal hard X-rays. Furthermore, we report serendipitous discovery of X-ray super-flares occurring away from periastron, potentially associated with interacting magnetospheres. The current study is part of a broader multi-wavelength campaign, which is planned to investigate the influence of DQ Tau’s stellar radiation on gas-phase ion chemistry within its circumbinary disk. Keywords: Pre-main sequence stars (1290) — Spectroscopic binary stars (1557) — X-ray stars (1823) — Stellar magnetic fields (1610) — Optical flares (1166) — Ultraviolet transient sources (1854) —- Stellar x-ray flares (1637) — Solar x-ray flares (1816) — Solar flares (1496) — Stellar flares (1603) — Protoplanetary disks (1300) 1. INTRODUCTION DQ Tau is a nearby (D = 195 pc; Gaia Collaboration et al. 2023), non-eclipsing, double-lined spectro scopic binary system, consisting of two pre-main sequence (PMS) stars of equal mass (0.6 M⊙) and equal radius (2 R⊙) (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). These stars exhibit spectral types within the range of M0 to K7. The rotational periods of the primary and secondary components are 3 days (K´osp´al et al. 2018) and 4.5 days (Pouilly et al. in prep.), respectively. The orbital period measures 15.8 days. DQ Tau boasts a highly eccentric orbit (e ∼ 0.6) and displays an exceptionally small periastron separation, measuring only about 8 −10 stellar radii (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). Furthermore, the binary components of DQ Tau harbor relatively strong surface magnetic fields, estimated at around 2.5 kG, which give rise to formidable magnetospheres (Pouilly et al. 2023, Pouilly et al. in prep.). Surrounding DQ Tau is a protoplanetary disk of average size (≤ 100 au), complete with a small 0.3 au cavity (Czekala et al. 2016; K´osp´al et al. 2018; Ballering & Eisner 2019). Large optical and UV brightenings primarily occur at orbital phase (Φ = 0.8 − 1.2), and they are mainly attributed to the pulsed accretion of disk material onto the binary components (Tofflemire et al. 2017; K´osp´al et al. 2018; Muzerolle et al. 2019; Fiorellino et al. 2022). However, far-UV (FUV) observations of DQ Tau with the Cosmic Origins Spectrograph onboard HST (HST-COS) showed no correlation between the orbital phase of the binary and the C IV flux, a tracer of mass accretion rate, indicating that some component of the behavior is stochastic (Ardila et al. 2015). The system exhibits powerful mm/X-ray flares coinciding with periastron passage, attributed to collisions between the magnetospheres of the binary components. The evidence supporting the magnetsophere collision hypothesis includes the recurrence of synchrotron mmband flaring during 4 periastron encounters (Salter et al. 2008, 2010), the recurrence of soft X-ray flaring in 2 periastron encounters (Getman et al. 2011, 2022a), the timing and energy relationships between the mm and X-ray flares, and the consistency observed between the flare loop size and binary separation (Salter et al. 2010; Getman et al. 2011). Several other young binary systems with high eccentricities have been reported to exhibit enhanced levels of either X-ray, optical/mm, or radio emissions near their periastron passages. Notable examples include ϵ Lupi (Das et al. 2023), a collective study of four binaries (Parenago 523, RX J1622.7-2325 Nw, UZ Tau E, and HD 152404) conducted by Getman et al. (2016), UZ Tau E (K´osp´al et al. 2011), and V773 Tau A (Massi et al. 2008; Adams et al. 2011). In these systems, magnetosphere collision has been proposed as a primary mechanism responsible for generating the magnetic reconnection energy that drives these events. Modelling studies have shown that PMS X-rays have strong impact on disk ionization and chemistry (Glassgold et al. 2000; Alexander et al. 2014). But most studies assume continuous irradiation without considering the high-amplitude variations in flux and spectrum due to super-flares. A few time-dependent calculations show that disk ionization may respond to sudden large Xray flares (Rab et al. 2017; Waggoner & Cleeves 2022). This may already have been seen. One empirical report of ionization variability has emerged: the H13CO+ abundance of IM Lup’s disk jumped up and down by a factor of 2.5 over months (Cleeves et al. 2017). However, since no concurrent X-ray observations were conducted, the exact cause cannot be definitively ascribed to X-ray flaring. The occurrence of large X-ray flares in young stars is relatively rare and unpredictable, displaying a stochastic nature (Getman & Feigelson 2021). However, the presence of predictable X-ray super-flares and accretion outbursts in close proximity to periastron passage makes DQ Tau an extraordinary laboratory for investigating the influence of stellar radiation on the gas-phase ion chemistry within its disk. To achieve this, we conducted a single X-ray observation using the NuSTAR telescope, along with multiple short-duration observations using the Swift telescope in the X-ray, UV, and optical wavelengths. These observations were strategically timed near a specific periastron passage of DQ Tau, taking place in July-August 2022. Additionally, we captured multiple snapshots of the non-periastron portion of DQ Tau’s orbit using the Chandra X-ray telescope. However, we encountered unfavorable weather conditions that limited our ALMA observations to a single short session near periastron. The results from this ALMA observation will be presented in a forthcoming paper. Meanwhile, building upon the X-ray/UV/optical data acquired in 2022 and the previously obtained X-ray/mm data (Salter et al. 2010; Getman et al. 2011, 2022a), our current study is dedicated to further investigating the origins and energetic properties of X-ray flares, along with their corresponding near-ultraviolet (NUV) and optical flare counterparts, observed within the remarkable young binary system DQ Tau. The structure of the paper is outlined as follows: Section 2 provides a detailed description of the data reduction and reduction procedures employed for X-ray, UV, and optical analyses. In Section 3, we present the detection of flares and examine their spectral properties. Section 4 offers a comparison between the X-ray periastron flares observed in DQ Tau and super-flares observed in numerous other PMS stars. Finally, Section 5 delves into a discussion surrounding the origin and energetics of the X-ray flares, as well as their associated NUV and optical counterparts. This paper is available on arxiv under CC 4.0 license. Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro X-ray Observations and Data Extraction Flare Analyses Comparison with X-ray Flares from Young Stars Discussion Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro X-ray Observations and Data Extraction X-ray Observations and Data Extraction Flare Analyses Flare Analyses Comparison with X-ray Flares from Young Stars Comparison with X-ray Flares from Young Stars Discussion Discussion Conclusions Conclusions Acknowledgments and References Acknowledgments and References ABSTRACT DQ Tau is a unique young high-eccentricity binary system that exhibits regular magnetic reconnection flares and pulsed accretion near periastron. We conducted NuSTAR, Swift, and Chandra observations during the July 30, 2022 periastron to characterize X-ray, near-ultraviolet (NUV), and optical flaring emissions. Our findings confirm the presence of X-ray super-flares accompanied by substantial NUV and optical flares, consistent with previous discoveries of periastron flares in 2010 and 2021. These observations, supported by new evidence, strongly establish the magnetosphere collision mechanism as the primary driver of magnetic energy release during DQ Tau’s periastron flares. The energetics of the observed X-ray super-flares remain consistent across the three periastrons, indicating recurring energy sources during each passage, surpassing the capabilities of single stars. The observed flaring across multiple bands supports the Adams et al. model for magnetosphere interaction in eccentric binaries. Evidence from modeling and past and current observations suggests that both the mm/X-ray periastron flares and tentatively, the magnetic reconnection-related components of the optical/NUV emissions, conform to the classical solar/stellar non-thermal thick-target model, except for the distinctive magnetic energy source. However, our N u STAR observations suffered from high background levels, hindering the detection of anticipated non-thermal hard X-rays. Furthermore, we report serendipitous discovery of X-ray super-flares occurring away from periastron, potentially associated with interacting magnetospheres. The current study is part of a broader multi-wavelength campaign, which is planned to investigate the influence of DQ Tau’s stellar radiation on gas-phase ion chemistry within its circumbinary disk. u Keywords : Pre-main sequence stars (1290) — Spectroscopic binary stars (1557) — X-ray stars (1823) — Stellar magnetic fields (1610) — Optical flares (1166) — Ultraviolet transient sources (1854) —- Stellar x-ray flares (1637) — Solar x-ray flares (1816) — Solar flares (1496) — Stellar flares (1603) — Protoplanetary disks (1300) Keywords 1. INTRODUCTION DQ Tau is a nearby (D = 195 pc; Gaia Collaboration et al. 2023), non-eclipsing, double-lined spectro scopic binary system, consisting of two pre-main sequence (PMS) stars of equal mass (0.6 M⊙) and equal radius (2 R⊙) (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). These stars exhibit spectral types within the range of M0 to K7. The rotational periods of the primary and secondary components are 3 days (K´osp´al et al. 2018) and 4.5 days (Pouilly et al. in prep.), respectively. The orbital period measures 15.8 days. DQ Tau boasts a highly eccentric orbit (e ∼ 0.6) and displays an exceptionally small periastron separation, measuring only about 8 −10 stellar radii (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). Furthermore, the binary components of DQ Tau harbor relatively strong surface magnetic fields, estimated at around 2.5 kG, which give rise to formidable magnetospheres (Pouilly et al. 2023, Pouilly et al. in prep.). Surrounding DQ Tau is a protoplanetary disk of average size (≤ 100 au), complete with a small 0.3 au cavity (Czekala et al. 2016; K´osp´al et al. 2018; Ballering & Eisner 2019). Large optical and UV brightenings primarily occur at orbital phase (Φ = 0.8 − 1.2), and they are mainly attributed to the pulsed accretion of disk material onto the binary components (Tofflemire et al. 2017; K´osp´al et al. 2018; Muzerolle et al. 2019; Fiorellino et al. 2022). However, far-UV (FUV) observations of DQ Tau with the Cosmic Origins Spectrograph onboard HST (HST-COS) showed no correlation between the orbital phase of the binary and the C IV flux, a tracer of mass accretion rate, indicating that some component of the behavior is stochastic (Ardila et al. 2015). The system exhibits powerful mm/X-ray flares coinciding with periastron passage, attributed to collisions between the magnetospheres of the binary components. The evidence supporting the magnetsophere collision hypothesis includes the recurrence of synchrotron mmband flaring during 4 periastron encounters (Salter et al. 2008, 2010), the recurrence of soft X-ray flaring in 2 periastron encounters (Getman et al. 2011, 2022a), the timing and energy relationships between the mm and X-ray flares, and the consistency observed between the flare loop size and binary separation (Salter et al. 2010; Getman et al. 2011). Several other young binary systems with high eccentricities have been reported to exhibit enhanced levels of either X-ray, optical/mm, or radio emissions near their periastron passages. Notable examples include ϵ Lupi (Das et al. 2023), a collective study of four binaries (Parenago 523, RX J1622.7-2325 Nw, UZ Tau E, and HD 152404) conducted by Getman et al. (2016), UZ Tau E (K´osp´al et al. 2011), and V773 Tau A (Massi et al. 2008; Adams et al. 2011). In these systems, magnetosphere collision has been proposed as a primary mechanism responsible for generating the magnetic reconnection energy that drives these events. Modelling studies have shown that PMS X-rays have strong impact on disk ionization and chemistry (Glassgold et al. 2000; Alexander et al. 2014). But most studies assume continuous irradiation without considering the high-amplitude variations in flux and spectrum due to super-flares. A few time-dependent calculations show that disk ionization may respond to sudden large Xray flares (Rab et al. 2017; Waggoner & Cleeves 2022). This may already have been seen. One empirical report of ionization variability has emerged: the H13CO+ abundance of IM Lup’s disk jumped up and down by a factor of 2.5 over months (Cleeves et al. 2017). However, since no concurrent X-ray observations were conducted, the exact cause cannot be definitively ascribed to X-ray flaring. The occurrence of large X-ray flares in young stars is relatively rare and unpredictable, displaying a stochastic nature (Getman & Feigelson 2021). However, the presence of predictable X-ray super-flares and accretion outbursts in close proximity to periastron passage makes DQ Tau an extraordinary laboratory for investigating the influence of stellar radiation on the gas-phase ion chemistry within its disk. To achieve this, we conducted a single X-ray observation using the N u STAR telescope, along with multiple short-duration observations using the Swift telescope in the X-ray, UV, and optical wavelengths. These observations were strategically timed near a specific periastron passage of DQ Tau, taking place in July-August 2022. Additionally, we captured multiple snapshots of the non-periastron portion of DQ Tau’s orbit using the Chandra X-ray telescope. However, we encountered unfavorable weather conditions that limited our ALMA observations to a single short session near periastron. The results from this ALMA observation will be presented in a forthcoming paper. u Meanwhile, building upon the X-ray/UV/optical data acquired in 2022 and the previously obtained X-ray/mm data (Salter et al. 2010; Getman et al. 2011, 2022a), our current study is dedicated to further investigating the origins and energetic properties of X-ray flares, along with their corresponding near-ultraviolet (NUV) and optical flare counterparts, observed within the remarkable young binary system DQ Tau. The structure of the paper is outlined as follows: Section 2 provides a detailed description of the data reduction and reduction procedures employed for X-ray, UV, and optical analyses. In Section 3, we present the detection of flares and examine their spectral properties. Section 4 offers a comparison between the X-ray periastron flares observed in DQ Tau and super-flares observed in numerous other PMS stars. Finally, Section 5 delves into a discussion surrounding the origin and energetics of the X-ray flares, as well as their associated NUV and optical counterparts.

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Abstract and Intro

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords: Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release. Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords : Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) Keywords 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release.

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Abstract and Intro

As companies welcome the exciting benefits of Generative AI for getting more work done, they also face a tricky problem: Gen AI Hallucinations. Ideally, AI-generated results should be reliable without human assistance. But sometimes, AI can hallucinate or generate fictitious content that poses a great risk. When wrong information gets out, it doesn't just cause embarrassment—it can hurt a company's reputation and erode people’s trust. So, while everyone wants to use Gen AI to be more efficient, they must also be careful to avoid these mistakes that could damage their image. What is a Gen AI Hallucination? , seen in Large-Language Models (LLMs) like ChatGPT, often leads to inaccurate information and responses, ranging from slight deviations from facts to entirely fabricated content. Gen AI hallucination LLMs operate by predicting the next word in a response based on the user's input, lacking independent reasoning abilities, which can result in errors. Given the natural fluency and coherence of the generated text, fact-checking is essential to prevent the . dissemination of false information Example: An infamous incident of AI hallucination occurred with Bard when asked about discoveries from the James Webb Space Telescope. Bard claimed the telescope was the first to capture images of an exoplanet outside our solar system. But this information was false and was quickly debunked through fact-checking. Why Do Gen AI Hallucinations Happen? Hallucinations in generative AI models often stem from the following: Irrelevant or subpar information fetched by the retrieval system can degrade output quality, causing errors or misinformed responses. Inaccurate context retrieval: Poor user prompts can mislead the LLM, resulting in responses based on wrong or unsuitable context. Additionally, if the underlying app prompt is ineffective this could also hurt the GenAI app’s information retrieval process. Ineffective prompts: Difficulty with idioms, slang, or non-English languages may result in incorrect or illogical responses, especially if the retrieval system fails to obtain or decipher relevant context in these languages. Complex language challenges: Why Are Gen AI Hallucinations a Problem? AI hallucinations pose serious risks beyond technical errors, . Let’s briefly examine all the problems caused by Generative AI hallucinations. impacting your brand's reputation and consumer trust Reputational Damage The reputation and status of the impacted party may be seriously damaged when AI-generated content is deliberately used to propagate false information or defamatory statements. This involves a bad reputation, a decline in trust, and financial consequences. It can be difficult to reverse or undo the harm caused once false information has been disseminated, which can have a long-lasting impact on relationships with partners, consumers, and the general public. Legal Consequences If we look at the legal aspects of AI hallucinations, we’ll find tons of them. These legal consequences range from defamation, invasion of privacy, or copyright infringement. This could also happen unintentionally. Imagine you’re just exploring AI but end up creating something that someone doesn’t. In that case, you’ll have to face legal charges if it becomes viral. A few years back in 2019, one such event happened when a went viral. He was caught on camera making offensive and untrue remarks. The company's stock price dropped significantly, but it was a joke. After that, the business took the makers of the deepfake video to court. deepfake video claiming to be Mark Zuckerberg Medical Mistakes Hallucinations caused by generative AI can potentially cause medical errors by presenting false or misleading information. For example, errors in the data produced by the AI model may lead to a misdiagnosis or an ineffective course of therapy if medical personnel rely on AI-generated content for diagnostic or therapeutic suggestions. A study published in raised issues with commercial AI algorithms used to diagnose dermatology. The study discovered that these algorithms, intended to evaluate pictures of skin lesions, showed notable errors, especially when differentiating between benign and malignant lesions. This demonstrates how crucial it is to thoroughly validate and continuously assess AI algorithms in the healthcare industry. JAMA Network Open in 2020 Compliance Challenges Compliance issues can occur when AI hallucinations result in the creation of content that does not adhere to industry norms or legal constraints. For instance, businesses may face legal and financial repercussions if by unintentionally revealing private information. AI-generated material violates data privacy laws Creating biased or discriminating information against equality and fairness could be one way to do this. Organizations must implement robust governance frameworks and quality assurance processes to address compliance challenges associated with AI hallucinations. Financial Loss Hallucination in Artificial Intelligence can also result in financial loss. That occurs if inaccurate content creation causes expensive mistakes or interferes with corporate processes. For instance, if financial reports produced by AI are erroneous or contain misleading information. It forces the investors or companies to make poor investment decisions that result in losses. Similarly, inaccurate or misinterpreted customer preferences might cause AI-generated marketing initiatives to fall flat with target demographics. Thus wasting money and missing out on sales possibilities. Disruption of User Trust AI hallucinations can create fake information that closely mimics reality, which can seriously undermine consumer trust. This may result in disseminating false information, swaying public opinion, and growing fake news. Users will find it easier to trust sources and separate fact from fiction when AI-generated material is identical to real information. This erodes public confidence in disseminating information and may significantly affect public trust. Are Artificial Intelligence Hallucinations Always Bad? Hallucinations, despite their potential risks, can hold value. LLMs exhibit a unique set of strengths and weaknesses. While they may struggle with tasks traditionally associated with computer proficiency, such as search capabilities, they excel in storytelling, creativity, and aesthetics. Hallucinations in AI should be reframed as a feature rather than a flaw within AI systems. Marketers can leverage this by prompting AI to hallucinate its interface, enabling it to perform tasks that are otherwise challenging or costly to measure. For instance, marketers could assign scores to various objects based on their alignment with the brand and then task AI with identifying potential lifelong consumers based on those scores. Ultimately, manipulating these hallucinations could lead to innovative solutions and enhanced outcomes in the advertising and marketing space.

Gen AI Hallucinations: The Good, the Bad, and the Costly

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References ABSTRACT Terrestrial exoplanets orbiting M-dwarf stars are promising targets for transmission spectroscopy with existing or near-future instrumentation. The atmospheric composition of such rocky planets remains an open question, especially given the high X-ray and ultraviolet flux from their host M dwarfs that can drive atmospheric escape. The 1.3 R⊕ exoplanet GJ 486b (Teq ∼ 700 K), orbiting an M3.5 star, is expected to have one of the strongest transmission spectroscopy signals among known terrestrial exoplanets. We observed three transits of GJ 486b using three different high-resolution spectrographs: IRD on Subaru, IGRINS on Gemini-South, and SPIRou on the Canada-France-Hawai’i Telescope. We searched for atmospheric absorption from a wide variety of molecular species via the cross-correlation method, but did not detect any robust atmospheric signals. Nevertheless, our observations are sufficiently sensitive to rule out several clear atmospheric scenarios via injection and recovery tests, and extend comparative exoplanetology into the terrestrial regime. Our results suggest that GJ 486b does not possess a clear H2/He-dominated atmosphere, nor a clear 100% water-vapor atmosphere. Other secondary atmospheres with high mean molecular weights or H2/He-dominated atmospheres with clouds remain possible. Our findings provide further evidence suggesting that terrestrial planets orbiting M-dwarf stars may experience significant atmospheric loss. : Exoplanet atmospheres (487) — Planetary atmospheres (1244) — Exoplanets (498) — Exoplanet atmospheric composition (2021) Keywords 1. INTRODUCTION Exoplanets that transit M-dwarf stars produce relatively deep transits due to their high planet-star radius ratios. Consequently, it may be possible to detect biosignatures in the atmospheres of terrestrial exoplanets with existing or near-future instrumentation (e.g., Snellen et al. 2013; Wunderlich et al. 2019). However, the long-term habitability of such planets depends crucially on the stability of their atmospheres, which are subject to high X-ray + extreme UV (XUV) emissions during the pre-main-sequence phase of their M-dwarf hosts (e.g., Shkolnik & Barman 2014; Peacock et al. 2020) as well as flare events (e.g., Neves Ribeiro do Amaral et al. 2022) that can drive atmospheric escape (e.g., Vidal-Madjar et al. 2003; Bourrier et al. 2017a; Airapetian et al. 2017). Terrestrial exoplanets are expected to exhibit a rich diversity in atmospheric compositions. Exoplanets with Rp < 1.4 R⊕ and orbital periods < 100 days likely form as gas-rich sub-Neptunes that subsequently lose their primordial atmospheres due to photoevaporation (Rogers & Owen 2021). The amount of primordial atmosphere retained depends on several factors, such as their host star’s spectral energy distribution and activity levels, along with how much gas they accrete during formation (e.g., Bolmont et al. 2017; Owen et al. 2020). Should the primordial hydrogen-dominated atmosphere be lost, a secondary atmosphere may be retained. The composition of secondary atmospheres depends on a variety of processes, including interior outgassing, impact delivery, erosion, atmosphere-surface exchange, weathering, and volatile sequestration (e.g., Rogers et al. 2011). Driven by these processes, hot terrestrial planets (T & 1500 K) are predicted to have atmospheres containing atoms and ions, such as Na or Ca+, produced by surface vaporization (Schaefer & Fegley 2009) — though other hydrogen- or nitrogen-rich compositions are possible (Miguel 2019). However, cooler terrestrial planets are likely rich in molecular species, such as H2O, CO2, CO, O2, C2H2, and CH4 (e.g., Ramirez & Kaltenegger 2014; Madhusudhan et al. 2016; Kite & Schaefer 2021). Given the range of theoretical possibilities, observational constraints on the composition of terrestrial exoplanet atmospheres are crucially needed. While there have been several attempts to detect the atmospheres of a few terrestrial exoplanets, these have only yielded constraints from nondetections. For example, 55 Cancri e is a 1.9 R⊕ exoplanet with a temperature of approximately 2400 K due to its 17 hr orbit around its G8V host star (Morris et al. 2021). Hubble Space Telescope observations suggest that 55 Cancri e may possess a thick atmosphere (Tsiaras et al. 2016). However, phase curve observations with the Spitzer space telescope (Demory et al. 2016) and ground-based high-resolution transmission spectroscopy point toward 55 Cancri e not possessing a thick atmosphere or having no atmosphere at all (Ridden-Harper et al. 2016; Esteves et al. 2017; Jindal et al. 2020; Deibert et al. 2021a; Zhang et al. 2021). A similar result was found for the cooler 1.3 R⊕ exoplanet LHS 3844 b, which has an equilibrium temperature of 800 K and orbits an M5 host star (Vanderspek et al. 2019). Kreidberg et al. (2019) observed the phase curve of LHS 3844 b with the Spitzer space telescope and found that thick atmospheres with surface pressures greater than 10 bar could be ruled out, while less massive atmospheres would be susceptible to erosion by the stellar wind. Additionally, they found that a bare rock model fit their data well. Another candidate for atmospheric characterization that orbits an M-dwarf is GJ 1132b, which has a radius, mass, and equilibrium temperature of 1.1 R⊕, 1.7 M⊕, and 580 K, respectively (Berta-Thompson et al. 2015). Using transit observations from the Hubble Space Telescope (HST), Swain et al. (2021) inferred that GJ 1132b possesses a H/He-rich atmosphere with large-amplitude spectral features. However, using the same HST data, Libby-Roberts et al. (2022) and Mugnai et al. (2021) independently found a featureless transmission spectrum, possibly indicating an atmosphere with a high mean molecular weight (MMW), a high-altitude aerosol layer, or a near-total lack of an atmosphere. These contrasting results highlight the difficulty of characterizing the atmospheres of small planets, even with space-based instrumentation. Comprehensive searches for the atmospheres of the seven approximately Earth-sized planets that transit the M8V star TRAPPIST-1 have also been carried out. These transit observations with the Hubble and Spitzer space telescopes all point toward the absence of extended hydrogen-dominated atmospheres. However, denser, potentially habitable atmospheres are consistent with the observational limits for some TRAPPIST-1 planets (de Wit et al. 2016; Bourrier et al. 2017b,a; de Wit et al. 2018; Zhang et al. 2018; Wakeford et al. 2019; Gressier et al. 2022). GJ 486b[1] is a 1.3 R⊕ and 2.8 M⊕ exoplanet with an equilibrium temperature of about 700 K, an M3.5 V host star of J-band magnitude 7.2, and an orbital period of 1.5 days (Trifonov et al. 2021). These system properties make GJ 486b well suited to atmospheric characterization with transmission spectroscopy, as reflected by it having one of the largest transmission spectroscopy metrics (as defined by Kempton et al. 2018) of all known terrestrial planets (Trifonov et al. 2021). Therefore, GJ 486b is an ideal observational target to inform our understanding of terrestrial planets around M-dwarf stars. Previous studies have surmised that a wide range of atmospheric compositions are possible for GJ 486b. Caballero et al. (2022) refined GJ 486b’s mass and radius, and presented a suite of interior and atmospheric models. They showed that possible atmospheres include: (i) a H/He-dominated atmosphere with solar abundances; (ii) a H/He atmosphere with enhanced metallicity (e.g., 100x solar abundances); (iii) a H2O-dominated atmosphere; (iv) a CO2-dominated atmosphere; or (v) bare rock with a tenuous atmosphere, possibly containing Na. Geochemical models based on initial conditions representative of a range of possible planets suggest that a H2O-dominated atmosphere is the most likely outcome for a 3 M⊕ planet like GJ 486b (Ortenzi et al. 2020). However, carbonaceous chondrite outgassing measurements suggest that CO2-dominated atmospheres could also evolve on terrestrial planets (Thompson et al. 2021). Differentiating between these scenarios requires spectroscopic observations. Molecular species such as those expected to be in GJ 486b’s atmosphere produce dense forests of spectral lines that can be individually resolved with high-resolution spectroscopy. The signals from these lines can be combined, even if they are individually hidden in the noise, by cross-correlating the data with a template containing the molecular lines (e.g., Snellen et al. 2010; Birkby 2018). This powerful technique has facilitated the detection of species such as H2O, CO, HCN, CH4, NH3, and C2H2 in the transmission spectra of hot Jupiters (e.g., Giacobbe et al. 2021). As high-resolution spectroscopy probes line cores, which contain information about the upper (low-pressure) regions of exoplanet atmospheres, it is highly complementary to low-resolution observations from telescopes such as the JWST, which probe higher pressures (Brogi & Line 2019). JWST is scheduled to observe two transits and eclipses of GJ 486b in programs GO 1981[2] (P.I.: K. Stevenson) and GO 1743[3] (P. I.: M. Mansfield), respectively. Here, we present the first high-resolution transmission spectroscopy observations of GJ 486b. We use three transits to constrain GJ 486b’s atmospheric composition. In what follows, in Sections 2 and 3 we describe our observations and data reduction approach. Section 4 describes our model spectra, while Section 5 describes our cross-correlation methodology. Section 6 presents our results, while Section 8 discusses the implications of our results for the upcoming JWST observations. Finally, Section 9 offers concluding remarks. [1] Alternative names include Wolf 437b and TOI 1827b. [2] program-information.html?id=1981 https://www.stsci.edu/jwst/science-execution/ [3] program-information.html?id=1743 https://www.stsci.edu/jwst/science-execution/

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Abstract & Intro

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 2. OBSERVATIONS We used three different instruments to observe three separate transit events of GJ 486b. The first transit was observed with the Infrared Doppler (IRD) instrument on the 8.2 m Subaru telescope, providing a wavelength coverage of 0.97-1.75 µm at a resolving power of R = 70k (Tamura et al. 2012; Kotani et al. 2018). The second transit was observed with the Immersion GRating INfrared Spectrometer (IGRINS) on the 8.1 m GeminiSouth telescope (program ID: GS-2021A-DD-105), providing a wavelength coverage of 1.45-2.45 µm at a resolving power of R = 45k (Park et al. 2014; Lee & Gullikson 2016; Mace et al. 2018). The third transit was observed with the SPectropolarim`etre InfraROUge (SPIRou) on the 3.6 m Canada-France-Hawai’i Telescope (CFHT) in queue mode (run ID: 21BC34), providing a wavelength coverage of 0.98-2.35 µm at a resolving power of R = 75k (Artigau et al. 2014). The observation start time, duration, integration time, cadence, total number of frames, number of frames in transit, number of frames out of transit, and average signal-to-noise (S/N) ratio per pixel for these observations are shown in Table 1.

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