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 is used to solve the field equations, where and 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 and Pantheon, by applying the root mean square error (RMSE) formula.
For the approximate best fit values obtained from the model parameters, we observe that the deceleration parameter exhibits a signature-flipping (transition) point within the range 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.
{"title":"Parameterized Hubble parameter with observational constraints in fractal gravity","authors":"D.K. Raut , D.D. Pawar , A.P. Kale , 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}
Pub Date : 2025-03-01DOI: 10.1016/j.ascom.2025.100953
A. Prosvetov, A. Govorov, M. Pupkov, A. Andreev, V. Nazarov
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.
{"title":"Illuminating the Moon: Reconstruction of lunar terrain using photogrammetry, Neural Radiance Fields, and Gaussian Splatting","authors":"A. Prosvetov, A. Govorov, M. Pupkov, A. Andreev, 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}
Pub Date : 2025-02-25DOI: 10.1016/j.ascom.2025.100941
Felipe Ortiz , Raquel Pezoa , Michel Curé , Ignacio Araya , Roberto O.J. Venero , Catalina Arcos , Pedro Escárate , Natalia Machuca , Alejandra Christen
This work presents a multi-stage method for estimating wind parameters in the domain of massive stars. We use the H non-rotating synthetic spectral lines from the ISOSCELES database’s -slow solutions to train a Gaussian Mixture Model-based cluster method and a deep neural network classifier. Then, the observed H 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: and . 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.
{"title":"A multi-stage machine learning-based method to estimate wind parameters from Hα lines of massive stars","authors":"Felipe Ortiz , Raquel Pezoa , Michel Curé , Ignacio Araya , Roberto O.J. Venero , Catalina Arcos , Pedro Escárate , Natalia Machuca , 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}
Pub Date : 2025-02-17DOI: 10.1016/j.ascom.2025.100940
Z.A. Mabrouk, F.A. Abd El-Salam, A. Owis, Wesam Elmahy
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.
{"title":"Semi-analytical computation of commensurate semimajor axes of resonant orbits including second-order gravitational perturbations","authors":"Z.A. Mabrouk, F.A. Abd El-Salam, A. Owis, 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}
Pub Date : 2025-02-10DOI: 10.1016/j.ascom.2025.100939
Kapil , Lokesh Kumar Sharma , Anil Kumar Yadav
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-Lematre-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 (). We include the statefinder pair , which emulates the CDM model in the future. Bayesian Statistics and 57 Hubble data points, 6 baryonic acoustic oscillations data points, and 1048 Pantheon Type Ia supernovae data points are used to extract model constraints. Bayesian and 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.
{"title":"Observational constraints using Bayesian Statistics and deep learning in Kaniadakis holographic dark energy","authors":"Kapil , Lokesh Kumar Sharma , 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>></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}
Pub Date : 2025-02-10DOI: 10.1016/j.ascom.2025.100936
Eleonora Alei , Silvia Marinoni , Andrea Bignamini , Riccardo Claudi , Marco Molinaro , Martina Vicinanza , Serena Benatti , Ilaria Carleo , Avi Mandell , Franziska Menti , Angelo Zinzi
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 Exo-MerCat a script that collects information from multiple sources and creates a homogenized table. In this manuscript, we announce the release of the Exo-MerCat 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 Exo-MerCat 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.
{"title":"Exo-MerCat v2.0.0: Updates and open-source release of the Exoplanet Merged Catalog software","authors":"Eleonora Alei , Silvia Marinoni , Andrea Bignamini , Riccardo Claudi , Marco Molinaro , Martina Vicinanza , Serena Benatti , Ilaria Carleo , Avi Mandell , Franziska Menti , 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}
Pub Date : 2025-02-08DOI: 10.1016/j.ascom.2025.100937
T. Glauch , K. Tchiorniy
Gamma rays measured by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope 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 Fermi-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.
{"title":"flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data","authors":"T. Glauch , 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}
Pub Date : 2025-02-08DOI: 10.1016/j.ascom.2025.100938
J. Praveen, S.K. Narasimhamurthy
This study explores black hole dynamics within the framework of Finsler geometry, emphasizing the influence of the Finslerian constant and the quintessence dark energy parameter . By extending the conventional black hole metric with Finslerian corrections, we employ the osculating Riemannian approach and Barthel connection to derive a novel black hole metric, termed the Finsler Black Hole metric. The research investigates the effects of these modifications on photon trajectories, gravitational lensing, and shadow formation. The findings indicate that increasing enhances gravitational lensing, reduces the black hole shadow size, and produces distinct photon ring patterns. The analysis also reveals that the effective potential significantly influences photon orbits, with higher values pulling the photon sphere closer to the black hole. Furthermore, the study examines static and infalling spherical accretion models, showing that variations in and substantially impact the intensity distribution and geometry of black hole shadows. The inclusion of the Finslerian vector field adds complexity to the gravitational dynamics. These modifications introduce observable features that distinguish Finslerian black holes from those predicted by General Relativity. Additionally, the incorporation of dark energy through is shown to affect gravitational behavior and observable phenomena such as bending angles and critical impact parameters. This work provides a geometrical framework for understanding the interplay between Finsler geometry, dark energy, and black hole physics.
{"title":"Exploring the impact of dark energy in Finslerian black hole dynamics and observational features","authors":"J. Praveen, S.K. Narasimhamurthy","doi":"10.1016/j.ascom.2025.100938","DOIUrl":"10.1016/j.ascom.2025.100938","url":null,"abstract":"<div><div>This study explores black hole dynamics within the framework of Finsler geometry, emphasizing the influence of the Finslerian constant <span><math><mi>η</mi></math></span> and the quintessence dark energy parameter <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>. By extending the conventional black hole metric with Finslerian corrections, we employ the osculating Riemannian approach and Barthel connection to derive a novel black hole metric, termed the Finsler Black Hole metric. The research investigates the effects of these modifications on photon trajectories, gravitational lensing, and shadow formation. The findings indicate that increasing <span><math><mi>η</mi></math></span> enhances gravitational lensing, reduces the black hole shadow size, and produces distinct photon ring patterns. The analysis also reveals that the effective potential <span><math><mrow><mi>V</mi><mrow><mo>(</mo><mi>r</mi><mo>)</mo></mrow></mrow></math></span> significantly influences photon orbits, with higher <span><math><mi>η</mi></math></span> values pulling the photon sphere closer to the black hole. Furthermore, the study examines static and infalling spherical accretion models, showing that variations in <span><math><mi>η</mi></math></span> and <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> substantially impact the intensity distribution and geometry of black hole shadows. The inclusion of the Finslerian vector field <span><math><mi>β</mi></math></span> adds complexity to the gravitational dynamics. These modifications introduce observable features that distinguish Finslerian black holes from those predicted by General Relativity. Additionally, the incorporation of dark energy through <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> is shown to affect gravitational behavior and observable phenomena such as bending angles and critical impact parameters. This work provides a geometrical framework for understanding the interplay between Finsler geometry, dark energy, and black hole physics.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100938"},"PeriodicalIF":1.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377083","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ascom.2025.100934
Tijmen de Haan
cosmosage is a natural-language assistant intended for a wide audience, from laypersons interested in cosmology to students, teachers, and professional cosmologists. cosmosage provides a novel way to access knowledge and reason about cosmology. Leveraging the power of advanced large language models (LLMs), cosmosage has learned from a vast corpus of open-access source texts, including textbooks and papers. cosmosage is found to be state-of-the-art on the narrow task of answering questions about cosmology, outperforming all general-purpose models. The model parameters and code are publicly available.
{"title":"cosmosage: A natural-language assistant for cosmology","authors":"Tijmen de Haan","doi":"10.1016/j.ascom.2025.100934","DOIUrl":"10.1016/j.ascom.2025.100934","url":null,"abstract":"<div><div><span>cosmosage</span> is a natural-language assistant intended for a wide audience, from laypersons interested in cosmology to students, teachers, and professional cosmologists. <span>cosmosage</span> provides a novel way to access knowledge and reason about cosmology. Leveraging the power of advanced large language models (LLMs), <span>cosmosage</span> has learned from a vast corpus of open-access source texts, including textbooks and papers. <span>cosmosage</span> is found to be state-of-the-art on the narrow task of answering questions about cosmology, outperforming all general-purpose models. The model parameters and code are publicly available.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100934"},"PeriodicalIF":1.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143268283","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ascom.2025.100932
O.P. Meena , P. Sachan , R. Pratap , P. Meena
In the present investigation, we conduct an analysis of periodic orbits within the context of the Copenhagen problem, emphasizing the dynamical behavior of a test particle subjected to the gravitational influence of two primary bodies of equal mass, which are in continuous rotation characterized by a constant angular velocity. By expanding upon the classical framework, we treat the primary bodies as non-spherical entities, thereby introducing the phenomenon of oblateness into the dynamical system under consideration. Employing the methodology of Fourier series, we articulate the characteristics of periodic orbits in proximity to the libration points and systematically evaluate the influence of the orbital parameter on the spatial dimensions and temporal periods of these orbits. Through the incorporation of terms extending to the third order in Fourier series method, we present a comprehensive representation of the parameter’s influence on the orbital attributes. The findings indicate that with an increase in , the dimensions of periodic orbits experience a substantial expansion, while their temporal periods demonstrate non-linear fluctuations. Variational graphs elucidate the correlation between and the time period , revealing distinct patterns for the various families of orbits under analysis. Moreover, the oblateness exhibited by the primary bodies engenders significant alterations in the geometrical characteristics, size, and time period of the orbits, thereby underscoring their pivotal influence on the dynamics of orbital motion.
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