Astronomy and Computing最新文献

{"title":"Africanus IV. The Stimela2 framework: Scalable and repeatable workflows, from local to cloud compute","authors":"O.M. Smirnov ,&nbsp;S. Makhathini ,&nbsp;J.S. Kenyon ,&nbsp;H.L. Bester ,&nbsp;S.J. Perkins ,&nbsp;A.J.T. Ramaila ,&nbsp;B.V. Hugo","doi":"10.1016/j.ascom.2025.100959","DOIUrl":"10.1016/j.ascom.2025.100959","url":null,"abstract":"<div><div><span>Stimela2</span> is a new-generation framework for developing data reduction workflows. It is designed for radio astronomy data but can be adapted for other data processing applications. <span>Stimela2</span> aims at the middle ground between ease of development, human readability, and enabling robust, scalable and repeatable workflows. <span>Stimela2</span> defines a YAML-based domain specific language (DSL), which represents workflows by linear, concise and intuitive YAML-format <em>recipes</em>. Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format <em>cab definitions</em> detailing each task’s <em>schema</em> (inputs and outputs). The <span>Stimela2</span> DSL provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. The container images are open-source and maintained through a companion package called <span>cult-cargo</span>. This enables the development of system-agnostic and repeatable workflows. <span>Stimela2</span> facilitates the deployment of scalable, distributed workflows by interfacing with the <span>Slurm</span> scheduler and the <span>Kubernetes</span> API. The latter allows workflows to be readily deployed in the cloud. Previous papers in this series used <span>Stimela2</span> as the underlying technology to run workflows on the AWS cloud.</div><div>This paper presents an overview of <span>Stimela2</span>’s design, architecture and use in the radio astronomy context.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100959"},"PeriodicalIF":1.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of ghost dark energy on cosmic evolution in f(Q, L m) theory","authors":"M. Zeeshan Gul ,&nbsp;M. Sharif ,&nbsp;S.A. Qureshi","doi":"10.1016/j.ascom.2025.100956","DOIUrl":"10.1016/j.ascom.2025.100956","url":null,"abstract":"<div><div>The primary aim of this research is to explore the ghost dark energy model in the framework of <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) gravity, where <span>Q</span> represents the non-metricity scalar and <span>L</span> _m denotes the matter-Lagrangian density. To achieve this objective, we investigate the homogeneous and isotropic universe with an ideal matter distribution. We examine a scenario with interacting fluids that encompass both dark energy and dark matter in this context. Further, we reconstruct <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) model to examine the effects of this extended gravitational framework on the cosmic evolution. We explore the behavior of numerous cosmic parameters corresponding to distinct parametric values. The viability of the ghost dark energy model is evaluated by the matter contents, revealing that it supports the fast expansion of the cosmos. Furthermore, the statefinder <span><math><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>)</mo></mrow></math></span> and standard diagnostic pairs <span><math><mrow><mo>(</mo><msub><mrow><mi>ω</mi></mrow><mrow><mi>D</mi></mrow></msub><mo>−</mo><msubsup><mrow><mi>ω</mi></mrow><mrow><mi>D</mi></mrow><mrow><mo>′</mo></mrow></msubsup><mo>)</mo></mrow></math></span> are used to study the various cosmic eras. This study offers novel perspectives on the correlation between dark energy models and modified gravity theories, thereby enhancing our comprehension of cosmic evolution. Our results align with recent observational evidence, indicating that the <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) model effectively characterizes dark energy and cosmic evolution.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100956"},"PeriodicalIF":1.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Africanus I. Scalable, distributed and efficient radio data processing with Dask-MS and Codex Africanus","authors":"S.J. Perkins ,&nbsp;J.S. Kenyon ,&nbsp;L.A.L. Andati ,&nbsp;H.L. Bester ,&nbsp;O.M. Smirnov ,&nbsp;B.V. Hugo","doi":"10.1016/j.ascom.2025.100958","DOIUrl":"10.1016/j.ascom.2025.100958","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The physical configuration of new radio interferometers such as MeerKAT, SKA, ngVLA and DSA-2000 informs the development of software in two important areas. Firstly, tractably processing the sheer quantity of data produced by new instruments necessitates subdivision and processing on multiple nodes. Secondly, the sensitivity inherent in modern instruments due to improved engineering practices and greater data quantities necessitates the development of new techniques to capitalize on the enhanced sensitivity of modern interferometers.&lt;/div&gt;&lt;div&gt;This produces a critical tension in radio astronomy software development: a fully optimized pipeline is desirable for producing science products in a tractable amount of time, but the design requirements for such a pipeline are unlikely to be understood upfront in the context of artefacts unveiled by greater instrument sensitivity. Therefore, new techniques must continuously be developed to address these artefacts and integrated into a full pipeline. As Knuth reminds us, “Premature optimization is the root of all evil”. This necessitates a fundamental trade-off between a trifecta of (1) performant code (2) flexibility and (3) ease-of-development. At one end of the spectrum, rigid design requirements are unlikely to capture the full scope of the problem, while throw-away research code is unsuitable for production use.&lt;/div&gt;&lt;div&gt;This work proposes a framework for the development of radio astronomy techniques within the above trifecta. In doing so, we favour flexibility and ease-of-development over performance, but this does not necessarily mean that the software developed within this framework is slow. Practically this translates to using data formats and software from the Open Source Community. For example, by using &lt;span&gt;NumPy&lt;/span&gt; arrays and/or &lt;span&gt;Pandas&lt;/span&gt; dataframes, a plethora of algorithms immediately become available to the scientific developer.&lt;/div&gt;&lt;div&gt;Focusing on performance, the breakdown of Moore’s Law in the 2010s and the resultant growth of both multi-core and distributed (including cloud) computing, a fundamental shift in the writing of radio astronomy algorithms and the storage of data is required: It is necessary to &lt;em&gt;shard&lt;/em&gt; data over multiple processors and compute nodes, and to write algorithms that operate on these shards in parallel. The growth in data volumes compounds this requirement. Given the fundamental shift in compute architecture we believe this is central to the performance of any framework going forward, and is given especial emphasis in this one.&lt;/div&gt;&lt;div&gt;This paper describes two Python libraries, &lt;span&gt;Dask-MS&lt;/span&gt; and &lt;span&gt;codex africanus&lt;/span&gt; &lt;!--&gt; &lt;!--&gt;which enable the development of distributed High-Performance radio astronomy code with &lt;span&gt;Dask&lt;/span&gt;. &lt;span&gt;Dask&lt;/span&gt; is a lightweight Python parallelization and distribution framework that seamlessly integrates with the &lt;span&gt;PyData&lt;/span&gt; ecosystem to address radio astronomy “Big Data","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100958"},"PeriodicalIF":1.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameterized Hubble parameter with observational constraints in fractal gravity","authors":"D.K. Raut ,&nbsp;D.D. Pawar ,&nbsp;A.P. Kale ,&nbsp;N.G. Ghungarwar","doi":"10.1016/j.ascom.2025.100955","DOIUrl":"10.1016/j.ascom.2025.100955","url":null,"abstract":"<div><div>In the present paper, the dynamical aspects of the cosmological model of the Universe have been studied in fractal gravity, which is an effective quantum field theory. The parameterized Hubble parameter, given by <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow><mrow><mn>2</mn></mrow></mfrac><mrow><mo>(</mo><mn>1</mn><mo>+</mo><msup><mrow><mrow><mo>(</mo><mn>1</mn><mo>+</mo><mi>z</mi><mo>)</mo></mrow></mrow><mrow><mi>n</mi></mrow></msup><mo>)</mo></mrow><mo>,</mo></mrow></math></span> is used to solve the field equations, where <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and <span><math><mi>n</mi></math></span> are model parameters. We have obtained the approximate best-fit values of the model parameters using the least squares method, incorporating observational constraints from available datasets such as Hubble <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> and Pantheon, by applying the root mean square error (RMSE) formula.</div><div>For the approximate best fit values obtained from the model parameters, we observe that the deceleration parameter <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> exhibits a signature-flipping (transition) point within the range <span><math><mrow><mn>0</mn><mo>.</mo><mn>5</mn><mo>≤</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>d</mi><mi>a</mi></mrow></msub><mo>≤</mo><mn>1</mn><mo>.</mo><mn>668</mn><mo>,</mo></mrow></math></span> marking the transition from a decelerated universe to an accelerated expanding universe. In addition, we discuss various physical parameters, including pressure, energy density, and energy conditions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100955"},"PeriodicalIF":1.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illuminating the Moon: Reconstruction of lunar terrain using photogrammetry, Neural Radiance Fields, and Gaussian Splatting","authors":"A. Prosvetov,&nbsp;A. Govorov,&nbsp;M. Pupkov,&nbsp;A. Andreev,&nbsp;V. Nazarov","doi":"10.1016/j.ascom.2025.100953","DOIUrl":"10.1016/j.ascom.2025.100953","url":null,"abstract":"<div><div>Accurately reconstructing the lunar surface is critical for scientific analysis and the planning of future lunar missions. This study investigates the efficacy of three advanced reconstruction techniques – photogrammetry, Neural Radiance Fields, and Gaussian Splatting – applied to the lunar surface imagery. The research emphasizes the influence of varying illumination conditions and shadows, crucial elements due to the Moon's lack of atmosphere. Extensive comparative analysis is conducted using a dataset of lunar surface images captured under different lighting scenarios. Our results demonstrate the strengths and weaknesses of each method based on a pairwise comparison of the obtained models with the original one. The results indicate that using methods based on neural networks, it is possible to complement the model obtained by classical photogrammetry. These insights are invaluable for the optimization of surface reconstruction algorithms, promoting enhanced accuracy and reliability in the context of upcoming lunar exploration missions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100953"},"PeriodicalIF":1.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-stage machine learning-based method to estimate wind parameters from Hα lines of massive stars","authors":"Felipe Ortiz ,&nbsp;Raquel Pezoa ,&nbsp;Michel Curé ,&nbsp;Ignacio Araya ,&nbsp;Roberto O.J. Venero ,&nbsp;Catalina Arcos ,&nbsp;Pedro Escárate ,&nbsp;Natalia Machuca ,&nbsp;Alejandra Christen","doi":"10.1016/j.ascom.2025.100941","DOIUrl":"10.1016/j.ascom.2025.100941","url":null,"abstract":"<div><div>This work presents a multi-stage method for estimating wind parameters in the domain of massive stars. We use the H<span><math><mi>α</mi></math></span> non-rotating synthetic spectral lines from the ISOSCELES database’s <span><math><mi>δ</mi></math></span>-slow solutions to train a Gaussian Mixture Model-based cluster method and a deep neural network classifier. Then, the observed H<span><math><mi>α</mi></math></span> line profiles are deconvolved and classified into a class that provides a reduced subset of line profiles defined in ISOSCELES. This allows us to accurately and rapidly identify the closest line profile within the selected subset and obtain the wind parameters: <span><math><msub><mrow><mi>v</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> and <span><math><mover><mrow><mi>M</mi></mrow><mrow><mo>̇</mo></mrow></mover></math></span>. Compared to traditional methods, this multi-stage proposal significantly reduces the computation time required to determine the wind parameters and gives more accurate and objective results. Interesting results of this work include evaluating the method for a sample of 12 B-supergiants, offering a notable improvement in the fitting of the line profiles, as it allows for a better approximation of the shape of the P Cygni lines for both components, absorption, and emission.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100941"},"PeriodicalIF":1.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-analytical computation of commensurate semimajor axes of resonant orbits including second-order gravitational perturbations","authors":"Z.A. Mabrouk,&nbsp;F.A. Abd El-Salam,&nbsp;A. Owis,&nbsp;Wesam Elmahy","doi":"10.1016/j.ascom.2025.100940","DOIUrl":"10.1016/j.ascom.2025.100940","url":null,"abstract":"<div><div>This research work aims to understand how resonant geopotential harmonics affect the semi-major axis of GPS orbits. The study uses a second-order approximation to calculate iteratively the impact of higher zonal perturbations on the semi-major axis. In addition, Kaula's resonant perturbation theory is utilized to compute analytically the main resonant geopotential that can have significant effects on the motion. We derive and plot the drift rate as a function of the longitudinal position, aiming to identify stable and metastable positions at specific longitudes. The study also investigates motion around these points using the Poincare method, demonstrating the existence of periodic, quasi-periodic, and chaotic orbits near these positions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100940"},"PeriodicalIF":1.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observational constraints using Bayesian Statistics and deep learning in Kaniadakis holographic dark energy","authors":"Kapil ,&nbsp;Lokesh Kumar Sharma ,&nbsp;Anil Kumar Yadav","doi":"10.1016/j.ascom.2025.100939","DOIUrl":"10.1016/j.ascom.2025.100939","url":null,"abstract":"<div><div>In this paper, we present the Kaniadakis holographic dark energy (KHDE) model with hybrid expansion law, which describes the Universe accelerating expansion in the flat Friedmann-Lema<span><math><mover><mrow><mi>i</mi></mrow><mrow><mo>̃</mo></mrow></mover></math></span>tre-Robertson-Walker Universe. The deceleration parameter obtained in the KHDE model depicts the expansion of the universe from decelerating to an accelerating phase. The KHDE model’s equation of state (EoS) parameter reproduces the Cosmos’ rich behaviour, such as the phantom division line spanning the quintessence era (<span><math><mrow><mi>ω</mi><mo>&gt;</mo><mo>−</mo><mn>1</mn></mrow></math></span>). We include the statefinder pair <span><math><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>)</mo></mrow></math></span>, which emulates the <span><math><mi>Λ</mi></math></span> CDM model in the future. Bayesian Statistics and 57 Hubble data points, 6 baryonic acoustic oscillations <span><math><mrow><mo>(</mo><mi>B</mi><mi>A</mi><mi>O</mi><mo>)</mo></mrow></math></span> data points, and 1048 Pantheon Type Ia supernovae <span><math><mrow><mo>(</mo><mi>S</mi><mi>N</mi><mi>I</mi><mi>a</mi><mo>)</mo></mrow></math></span> data points are used to extract model constraints. Bayesian and <span><math><mrow><mi>A</mi><mi>N</mi><mi>N</mi></mrow></math></span> findings are also compared. CoLFI, an ANN-based parameter estimation approach is employed. CoLFI is more efficient for parameter estimation, especially for intractable likelihood functions or big, resource-intensive cosmological models. Some physical properties of the model are also discussed in detail.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100939"},"PeriodicalIF":1.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exo-MerCat v2.0.0: Updates and open-source release of the Exoplanet Merged Catalog software","authors":"Eleonora Alei ,&nbsp;Silvia Marinoni ,&nbsp;Andrea Bignamini ,&nbsp;Riccardo Claudi ,&nbsp;Marco Molinaro ,&nbsp;Martina Vicinanza ,&nbsp;Serena Benatti ,&nbsp;Ilaria Carleo ,&nbsp;Avi Mandell ,&nbsp;Franziska Menti ,&nbsp;Angelo Zinzi","doi":"10.1016/j.ascom.2025.100936","DOIUrl":"10.1016/j.ascom.2025.100936","url":null,"abstract":"<div><div>Exoplanet research is at the forefront of contemporary astronomy recommendations. As more and more exoplanets are discovered and vetted, databases and catalogs are built to collect information. Various resources are available to scientists for this purpose, though every one of them has different scopes and notations. In Alei et al. (2020) we described <span>Exo-MerCat</span> a script that collects information from multiple sources and creates a homogenized table. In this manuscript, we announce the release of the <span>Exo-MerCat</span> v2.0.0 script as an upgraded, tested, documented and open-source software to produce catalogs. The main upgrades on the script concern: (1) the addition of the TESS Input Catalog and the K2 Input Catalog as input sources; (2) the optimization of the main identifier queries; (3) a more complex merging of the entries from the input sources into the final catalog; (4) some quality-of-life improvements such as informative flags, more user-friendly column headers, and log files; (5) the refactoring of the code in modules. We compare the performance of <span>Exo-MerCat</span> v2.0.0 with the previous version and notice a substantial improvement in the completeness of the sample, thanks to the addition of new input sources, and its accuracy, because of the optimization of the script.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100936"},"PeriodicalIF":1.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data","authors":"T. Glauch ,&nbsp;K. Tchiorniy","doi":"10.1016/j.ascom.2025.100937","DOIUrl":"10.1016/j.ascom.2025.100937","url":null,"abstract":"<div><div>Gamma rays measured by the Large Area Telescope (LAT) on board the <em>Fermi Gamma-ray Space Telescope</em> tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves optimally use adaptive bin sizes in order to retrieve most information about the source dynamics and to combine gamma-ray observations in a multi-messenger perspective. However, standard adaptive binning approaches are slow, expensive and inaccurate in highly populated regions. Here, we present a novel, powerful, deep-learning-based approach to estimate the necessary time windows for adaptive binning light curves in <em>Fermi</em>-LAT data using raw photon data. The approach is shown to be fast and accurate. It can also be seen as a prototype to train machine-learning models for adaptive binning light curves for other astrophysical messengers.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100937"},"PeriodicalIF":1.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}