Astronomy and Computing最新文献

{"title":"Africanus I. Scalable, distributed and efficient radio data processing with Dask-MS and Codex Africanus","authors":"S.J. Perkins , J.S. Kenyon , L.A.L. Andati , H.L. Bester , O.M. Smirnov , B.V. Hugo","doi":"10.1016/j.ascom.2025.100958","DOIUrl":"10.1016/j.ascom.2025.100958","url":null,"abstract":"<div><div>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.</div><div>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.</div><div>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 <span>NumPy</span> arrays and/or <span>Pandas</span> dataframes, a plethora of algorithms immediately become available to the scientific developer.</div><div>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 <em>shard</em> 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.</div><div>This paper describes two Python libraries, <span>Dask-MS</span> and <span>codex africanus</span> <!--> <!-->which enable the development of distributed High-Performance radio astronomy code with <span>Dask</span>. <span>Dask</span> is a lightweight Python parallelization and distribution framework that seamlessly integrates with the <span>PyData</span> 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":"Unveiling the power of uncertainty: A journey into Bayesian Neural Networks for stellar dating","authors":"Víctor Tamames-Rodero ,&nbsp;Andrés Moya ,&nbsp;Luis Manuel Sarro ,&nbsp;Roberto Javier López-Sastre","doi":"10.1016/j.ascom.2025.100957","DOIUrl":"10.1016/j.ascom.2025.100957","url":null,"abstract":"<div><h3>Context:</h3><div>Astronomy and astrophysics demand rigorous handling of uncertainties to ensure the credibility of outcomes. The growing integration of artificial intelligence offers a novel avenue to address this necessity. This convergence presents an opportunity to create advanced models capable of quantifying diverse sources of uncertainty and automating complex data relationship exploration.</div></div><div><h3>What:</h3><div>We introduce a hierarchical Bayesian architecture whose probabilistic relationships are modeled by neural networks, designed to forecast stellar attributes such as mass, radius, and age (our main target). This architecture handles both observational uncertainties stemming from measurements and epistemic uncertainties inherent in the predictive model itself. As a result, our system generates distributions that encapsulate the potential range of values for our predictions, providing a comprehensive understanding of their variability and robustness.</div></div><div><h3>Methods:</h3><div>Our focus is on dating main sequence stars using a technique known as Chemical Clocks, which serves as both our primary astronomical challenge and a model prototype. In this work, we use hierarchical architectures to account for correlations between stellar parameters and optimize information extraction from our dataset. We also employ Bayesian neural networks for their versatility and flexibility in capturing complex data relationships.</div></div><div><h3>Results:</h3><div>By integrating our machine learning algorithm into a Bayesian framework, we have successfully propagated errors consistently and managed uncertainty treatment effectively, resulting in predictions characterized by broader uncertainty margins. This approach facilitates more conservative estimates in stellar dating. Our architecture achieves age predictions with a mean absolute error of less than 1 Ga for the stars in the test dataset.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100957"},"PeriodicalIF":1.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817371","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}

{"title":"Exploring the impact of dark energy in Finslerian black hole dynamics and observational features","authors":"J. Praveen,&nbsp;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}