Astronomy and Computing最新文献

{"title":"Galaxy morphological classification with manifold learning","authors":"Vasyl Semenov ,&nbsp;Vitalii Tymchyshyn ,&nbsp;Volodymyr Bezguba ,&nbsp;Maksym Tsizh ,&nbsp;Andrii Khlevniuk","doi":"10.1016/j.ascom.2025.100963","DOIUrl":"10.1016/j.ascom.2025.100963","url":null,"abstract":"<div><div>We address the problem of morphological classification of galaxies from the Galaxy Zoo DECaLS dataset using classical machine learning techniques. Our approach employs a dimensionality reduction method followed by a classical classifier to categorize galaxies based on shape (cigar/in-between/ round; edge-on/face-on) and texture (smooth/featured). We evaluate various dimensionality reduction methods, including Locally Linear Embedding (LLE), Isomap, Uniform Manifold Approximation and Projection (UMAP), t-SNE, and Principal Component Analysis (PCA). Our results demonstrate that most classical classifiers achieve their highest performance when combined with LLE, attaining accuracy comparable to that of simple neural networks. Moreover, in the case of shape classification, the three-dimensional representation remains interpretable, in contrast to the commonly observed loss of interpretability following nonlinear transformations. Additionally, we explore dimensionality reduction followed by k-means clustering to assess whether the data exhibits a natural tendency toward a specific number of clusters. We evaluate clustering performance using silhouette, elbow, Dunn, and Davies–Bouldin scores. While the Davies–Bouldin score indicates a slight preference for four clusters — closely aligning with classifications made by human astronomers — the other metrics do not support a distinct clustering structure.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100963"},"PeriodicalIF":1.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847794","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":"3D radio data visualisation in open science platforms for next-generation observatories","authors":"I. Labadie-García,&nbsp;J. Garrido,&nbsp;L. Verdes-Montenegro,&nbsp;M.Á. Mendoza,&nbsp;M. Parra-Royón,&nbsp;S. Sánchez-Expósito,&nbsp;R. Ianjamasimanana","doi":"10.1016/j.ascom.2025.100949","DOIUrl":"10.1016/j.ascom.2025.100949","url":null,"abstract":"<div><div>Next-generation telescopes will bring groundbreaking discoveries but they will also present new technological challenges. The Square Kilometre Array Observatory (SKAO) will be one of the most demanding scientific infrastructures, with a projected data output of 700 PB per year to be distributed to a network of SKA Regional Centres. Current tools are not fully suited to manage such massive data volumes, therefore, new research is required to transform science archives from data providers into service providers. In this paper we examine how a science archive can deliver advanced visualisation capabilities for the SKA science archive. In particular, we have conducted a thorough exploration of existing visualisation software for astronomy and other fields to identify tools capable of addressing Big Data requirements. Using selected technologies, we have developed a prototype archive that provides access to interactive visualisations of 3D radio data through web-based interfaces, adhering to International Virtual Observatory Alliance (IVOA) recommendations to favour interoperability and Open Science practices. In addition, we discuss how current IVOA recommendations support these visualisation capabilities and how they could be expanded. Our prototype archive includes a service to generate 3D models on the fly as a server operation, enabling remote visualisations in a flexible manner; for instance, a set of parameters can be used to customise the models and their visualisation. We have used SKA precursor and pathfinder data to test its usability and scalability, concluding that remote visualisation is a viable solution for handling high-volume data. However, our prototype is constrained by memory limitations, requiring techniques to reduce memory usage.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100949"},"PeriodicalIF":1.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829237","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":"LRS Bianchi type-V cosmological model in f(Q,T) theory of gravity with cold matter and holographic dark energy","authors":"Y.S. Solanke ,&nbsp;S. Mhaske ,&nbsp;V.J. Dagwal ,&nbsp;D.D. Pawar","doi":"10.1016/j.ascom.2025.100961","DOIUrl":"10.1016/j.ascom.2025.100961","url":null,"abstract":"<div><div>In the present work Bianchi type <span><math><mi>V</mi></math></span> cosmological model with cold dark matter and holographic dark energy with <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>,</mo><mi>T</mi><mo>)</mo></mrow></mrow></math></span> theory of gravity is investigated, with <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>,</mo><mi>T</mi><mo>)</mo></mrow><mo>=</mo><mi>α</mi><mi>Q</mi><mo>+</mo><mi>β</mi><mi>T</mi></mrow></math></span>, where <span><math><mi>α</mi></math></span> and <span><math><mi>β</mi></math></span> are constants. To find the solution of the field equation we have used the anisotropic relation. Various physical and geometrical properties of the model have been investigated graphically.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100961"},"PeriodicalIF":1.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847798","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":"Coniferest: A complete active anomaly detection framework","authors":"M.V. Kornilov ,&nbsp;V.S. Korolev ,&nbsp;K.L. Malanchev ,&nbsp;A.D. Lavrukhina ,&nbsp;E. Russeil ,&nbsp;T.A. Semenikhin ,&nbsp;E. Gangler ,&nbsp;E.E.O. Ishida ,&nbsp;M.V. Pruzhinskaya ,&nbsp;A.A. Volnova ,&nbsp;S. Sreejith ,&nbsp;SNAD team","doi":"10.1016/j.ascom.2025.100960","DOIUrl":"10.1016/j.ascom.2025.100960","url":null,"abstract":"<div><div>We present <span>coniferest</span>, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100960"},"PeriodicalIF":1.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817370","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 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":"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":"Corrigendum to “Compression method for solar polarization spectra collected from Hinode SOT/SP observations” [Astronomy and Computing, Vol 51 (2025) 100929]","authors":"Jargalmaa Batmunkh ,&nbsp;Yusuke Iida ,&nbsp;Takayoshi Oba ,&nbsp;Haruhisa Iijima","doi":"10.1016/j.ascom.2025.100952","DOIUrl":"10.1016/j.ascom.2025.100952","url":null,"abstract":"","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100952"},"PeriodicalIF":1.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231955","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}