Astronomy and Computing最新文献

{"title":"The photo-gravitational concentric Sitnikov problem","authors":"M. Javed Idrisi ,&nbsp;M. Shahbaz Ullah","doi":"10.1016/j.ascom.2023.100764","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100764","url":null,"abstract":"<div><p>The current framework involves a configuration of two pairs of primary celestial bodies engaged in synchronized circular orbits around a central point of mass. Additionally, an infinitesimal mass is positioned along the <span><math><mi>z</mi></math></span>-axis, traversing through the system’s center of mass. This distinctive celestial scenario is designated as the <em>Concentric Sitnikov Problem</em>. Notably, it is postulated that the initial pair of primary bodies emit radiation, while the latter pair remains radiation-free. Within the scope of this study, we delve into an exploration of equilibrium points, periodic orbits, and the intriguing Newton–Raphson basins of convergence (N-R BoC) within the concentric Sitnikov model, all subject to the influence of radiation pressure. Remarkably, our investigation uncovers the presence of three equilibrium points, each exhibiting linear instability across the entire spectrum of mass parameter values <span><math><mrow><msup><mrow><mi>μ</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>.</mo><mn>25</mn><mo>)</mo></mrow></mrow></math></span>. To visually comprehend the character of these celestial paths, we employ a graphical analysis technique known as the first return map. Varied values of the mass parameter <span><math><msup><mrow><mi>μ</mi></mrow><mrow><mo>∗</mo></mrow></msup></math></span> lead to the generation of diverse families of periodic orbits encircling both the primary celestial bodies and their equilibrium positions. Lastly, we embark on an exploration of the intricacies of the N-R BoC, intimately connected with the equilibrium points within this proposed celestial model.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92073801","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":"Stellar parameter estimation in O-type stars using artificial neural networks","authors":"M. Flores R. ,&nbsp;L.J. Corral ,&nbsp;C.R. Fierro-Santillán ,&nbsp;S.G. Navarro","doi":"10.1016/j.ascom.2023.100760","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100760","url":null,"abstract":"<div><p><span>This work presents the results of the implementation of a deep learning<span> system capable of estimating the effective temperature and surface gravity of O-type stars. The proposed system was trained with a database of 5,557 synthetic spectra computed with the stellar atmosphere code CMFGEN that covers stars with </span></span><span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>eff</mi></mrow></msub></math></span> from <span><math><mo>∼</mo></math></span>20,000 K to <span><math><mo>∼</mo></math></span>58,000 K, <span><math><mrow><mi>l</mi><mi>o</mi><mi>g</mi><mrow><mo>(</mo><mi>L</mi><mo>/</mo><msub><mrow><mi>L</mi></mrow><mrow><mo>⊙</mo></mrow></msub><mo>)</mo></mrow></mrow></math></span> from 4.3 to 6.3 dex, log<!--> <span><math><mi>g</mi></math></span> from 2.4 to 4.2 dex, and mass from 9 to 120 <span><math><msub><mrow><mi>M</mi></mrow><mrow><mo>⊙</mo></mrow></msub></math></span><span>. Important advantages proposed in this paper include using a set of equivalent width measurements over the optical region of the stellar spectra, which avoids processing the full spectra with the inherent computational cost and allows it to apply the same trained system over different spectra resolutions. The validation of the system was performed by processing a sample of twenty O-type stars taken from the IACOB database, and a subgroup of eleven stars of those twenty taken from The Galactic O-Star Spectroscopic Catalog (GOSC) with lower resolution. As complementary work, we show the results of a synthetic spectra fitting process with the aim of simplifying the comparison with other estimations and parameter fitting from the literature.</span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711359","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":"GWDALI: A Fisher-matrix based software for gravitational wave parameter-estimation beyond Gaussian approximation","authors":"J.M.S. de Souza ,&nbsp;R. Sturani","doi":"10.1016/j.ascom.2023.100759","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100759","url":null,"abstract":"<div><p>We introduce <em>GWDALI</em><span><span>, a new Fisher-matrix, python based software that computes likelihood gradients to forecast parameter-estimation precision of arbitrary network of terrestrial gravitational wave detectors observing compact binary coalescences. The main new feature with respect to analogous software is to assess parameter uncertainties beyond Fisher-matrix </span>approximation, using the derivative approximation for Likelihood (DALI). The software makes optional use of the LSC algorithm library </span><span>LAL</span><span> and the stochastic sampling algorithm </span><span>Bilby</span>, which can be used to perform Monte-Carlo sampling of exact or approximate likelihood functions. As an example we show comparison of estimated precision measurement of selected astrophysical parameters for both the actual likelihood, and for a variety of its derivative approximations, which turn out particularly useful when the Fisher matrix is not invertible.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92066510","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":"Analysis tool for lunar crescent visibility criterion based on integrated lunar crescent database","authors":"M.S. Faid,&nbsp;M.S.A. Mohd Nawawi,&nbsp;M.H. Mohd Saadon","doi":"10.1016/j.ascom.2023.100752","DOIUrl":"10.1016/j.ascom.2023.100752","url":null,"abstract":"<div><p>The analysis of lunar crescent visibility criteria is vital to provide a comparative insight into lunar crescent visibility criteria performance in predicting the visibility of a lunar crescent and suitability for Hijri calendar determination. While there have been attempts to measure the performance of lunar crescent visibility criteria, these works are in a singular analysis and not a comparative manner, not based on an integrated database of lunar crescent visibility under standardized calculated astrometry, and some are biased towards their lunar crescent visibility criterion. This warrants new research on methods to analyse lunar crescent visibility criteria. Therefore, this research endeavour to develop an analysis tool for lunar crescent visibility criteria using an integrated lunar crescent visibility database. Lunar and solar geometrical positions are calculated using the Skyfield Python library. 8290 lunar crescent visibility records are collected as a reference for analysis. The analysis tool called HilalPy was developed in the form of a Python library, as it enables ease of integration into other software or web pages and has easier deployment onto various operating systems. HilalPy uses descriptive statistics, contradiction rate percentage, and regression analysis as its base analysis, making the calculated result comparable to other lunar crescent visibility criteria. HilalPy is hoped to provide insight into the future development of lunar crescent visibility criteria, particularly for calendrical purposes.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41871504","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":"C\u00002-GaMe: Classification of cluster galaxy membership with machine learning","authors":"D. Farid ,&nbsp;H. Aung ,&nbsp;D. Nagai ,&nbsp;A. Farahi ,&nbsp;E. Rozo","doi":"10.1016/j.ascom.2023.100743","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100743","url":null,"abstract":"<div><p>We present <span>C</span>lassification of <span>C</span>luster <span>Ga</span>laxy <span>Me</span>mbers (<span>C</span>\u0000<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-<span>GaMe</span><span>), a classification algorithm<span> based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using phase space information as input. We train and test </span></span><span>C</span>\u0000<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-<span>GaMe</span> with the galaxies from UniverseMachine mock catalog based on Multi-Dark Planck 2 N-body simulations. We show that probabilistic classification is superior to deterministic classification in estimating the physical properties of clusters, including density profiles and velocity dispersion. We propose a set of estimators to get an unbiased estimation of cluster properties. We demonstrate that <span>C</span>\u0000<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-<span>GaMe</span><span> can recover the distribution of orbiting and infalling galaxies’ position and velocity distribution with </span><span><math><mrow><mo>&lt;</mo><mn>1</mn><mtext>%</mtext></mrow></math></span><span> statistical error when using probabilistic predictions in the presence of interlopers in the projected phase space. Additionally, we demonstrate the robustness of trained models by applying them to a different simulation. Finally, adding a specific star formation rate and the ratio of the galaxy’s halo mass to the cluster’s halo mass as additional features improves the classification performance. We discuss potential applications of this technique to enhance cluster cosmology and galaxy quenching.</span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711461","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":"Effective detection of variable celestial objects using machine learning-based periodic analysis","authors":"N. Chihara ,&nbsp;T. Takata ,&nbsp;Y. Fujiwara ,&nbsp;K. Noda ,&nbsp;K. Toyoda ,&nbsp;K. Higuchi ,&nbsp;M. Onizuka","doi":"10.1016/j.ascom.2023.100765","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100765","url":null,"abstract":"<div><p>This paper tackles the problem of effectively detecting variable celestial objects whose brightness periodically changes over time. This problem is crucial in studying the evolution and structure of the universe and elucidating physical phenomena. The method by Sesar et al. is one of the popular approaches used in detecting variable celestial objects that uses statistical data of celestial time series, such as intrinsic variability <span><math><mi>σ</mi></math></span> and <span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, etc. However, since statistical data is an aggregation of celestial time series, the previous approaches do not take advantage of the periodicity, which is the inherent characteristic of variable celestial objects; it fails to find variable celestial objects effectively. To solve such a problem, we propose an approach to detecting variable celestial objects using periodic analysis. Our approach uses sparse modeling as periodic analysis since celestial time series is typically sparse and sparse modeling can effectively obtain periodicities of the celestial objects from sparse time series. By exploiting the periodicities of the celestial objects as features, we perform binary classification to estimate whether a celestial object is a variable celestial object. To show the effectiveness of our approach, we evaluated our approach using Hyper SuprimeCam (HSC) PDR2 dataset, and we confirmed that AUC of our approach is 0.939 while AUC of the previous approach is 0.750; our approach can more effectively detect variable celestial objects.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221313372300080X/pdfft?md5=82e0a8e142ae9d328be92439066727b1&pid=1-s2.0-S221313372300080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92066512","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":"Machine learning methods for the search for L&T brown dwarfs in the data of modern sky surveys","authors":"A. Avdeeva","doi":"10.1016/j.ascom.2023.100744","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100744","url":null,"abstract":"<div><p><span>According to various estimates, brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy. However, few of them are discovered and well-studied, both individually and as a population. Homogeneous and complete samples of brown dwarfs are needed for these kinds of studies. Due to their weakness, spectral studies of brown dwarfs are rather laborious. For this reason, creating a significant reliable sample of brown dwarfs, confirmed by spectroscopic observations, seems unattainable at the moment. Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule applied to a vast amount of survey data. In this work, we use </span>machine learning<span><span> methods such as Random Forest Classifier<span>, XGBoost, </span></span>SVM<span> Classifier and TabNet on PanStarrs DR1, 2MASS and WISE data to distinguish L and T brown dwarfs from objects of other spectral and luminosity classes. The explanation of the models is discussed. We also compare our models with classical decision rules, proving their efficiency and relevance.</span></span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711455","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":"Processing system for coherent dedispersion of pulsar radio emission","authors":"I.A. Girin,&nbsp;S.F. Likhachev,&nbsp;A.S. Andrianov,&nbsp;M.S. Burgin,&nbsp;M.V. Popov,&nbsp;A.G. Rudnitskiy,&nbsp;V.A. Soglasnov,&nbsp;V.A. Zuga","doi":"10.1016/j.ascom.2023.100754","DOIUrl":"10.1016/j.ascom.2023.100754","url":null,"abstract":"<div><p><span>Our study provides pulsar researchers with the possibility to utilize VLBI data for looking at pulsar radio emission with the extreme time resolution typical for broad band VLBI recorders. Pulsars emit micropulses and giant pulses shorter than a microsecond. The short pulse durations indicate high </span>brightness temperatures. This constrains the physical nature of the pulsar radio emission mechanism, whose theory is not yet completely understood.</p><p>Our paper describes a system for converting VLBI observation data using coherent dedispersion and compensation algorithms for two-bit signal sampling. Coherent dedispersion is the key to processing pulsar observations to obtain the highest temporal resolution. Correction for signal sampling makes it possible to eliminate parasitic effects that interfere with the analysis of pulsar diffraction patterns. A pipeline has been established that uses the developed converter and the Astro Space Center software correlator. It allows us to reprocess all Radioastron pulsar observations and conduct a search for giant pulses, which requires the highest temporal resolution possible.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45561110","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":"An innovative tool for automating classification of stellar variability through nonlinear data analytics","authors":"R. Syiemlieh ,&nbsp;P.R. Saleh ,&nbsp;D. Hazarika ,&nbsp;E. Saikia","doi":"10.1016/j.ascom.2023.100763","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100763","url":null,"abstract":"<div><p>Though Classical Cepheids, <span><math><mi>δ</mi></math></span>-Scuti, Eclipsing Binary, Long-Period variables, and RRLyraes are abundant in most of the clusters, automating the classification of the objects faces challenges. Since the rate at which the data has been getting accumulated is enormous, this automation of classification is paramount for carrying out appropriate analysis of the objects depending on the class it belongs to. Our results prove that the proposed tool for automating stellar classification not only reduces misclassification by up to 94.79% (in case of classification between multimode subclass of <span><math><mi>δ</mi></math></span>-Scuti and Mira subclass of Long-Period variables) but also improves reliability by as high as 78.35% (in case of conventionally misclassified pair of RRab subclass of RRLyrae and Fundamental Mode subclass of Classical Cepheids). Our random forest model has achieved a cross-validation accuracy of 0.88 with conventional statistical parameters coupled with tools of Nonlinear Dynamical Theory. It has achieved the highest precision and recalls for Long-Period variables of the Mira subclass (i.e., 0.99 &amp; 0.99) and the lowest for Eclipsing Binary of subclass contact (i.e., 0.81 &amp; 0.77). A positive improvement in accuracy rate by 7.3% is observed when compared with a model based on a conventional statistical platform. This proves the significance of introducing the proposed tools in devising an automated classification model for stellar variables.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92073777","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":"The dynamics of a hyperbolic solution in f(R,G) gravity","authors":"D. Rabha,&nbsp;R. Roy Baruah","doi":"10.1016/j.ascom.2023.100761","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100761","url":null,"abstract":"<div><p>In this study, our focus is on exploring the dynamics of the universe using a flat FLRW model within the framework of <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>R</mi><mo>,</mo><mi>G</mi><mo>)</mo></mrow></mrow></math></span> gravity. The specific function <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>R</mi><mo>,</mo><mi>G</mi><mo>)</mo></mrow><mo>=</mo><mi>ξ</mi><mi>R</mi><mo>+</mo><mi>λ</mi><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><msup><mrow><mi>G</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> is considered, with <span><math><mi>R</mi></math></span> and <span><math><mi>G</mi></math></span> representing the Ricci scalar and Gauss–Bonnet invariant, respectively. To obtain the solution to the gravitational field equations within the <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>R</mi><mo>,</mo><mi>G</mi><mo>)</mo></mrow></mrow></math></span> formalism, we adopt a specific form for the scale factor, denoted as <span><math><mrow><mi>a</mi><mo>=</mo><msup><mrow><mo>sinh</mo></mrow><mrow><mfrac><mrow><mn>1</mn></mrow><mrow><mi>α</mi></mrow></mfrac></mrow></msup><mrow><mo>(</mo><mi>β</mi><mi>t</mi><mo>)</mo></mrow></mrow></math></span> (Nagpal et al., 2019). Here, <span><math><mi>α</mi></math></span> and <span><math><mi>β</mi></math></span> are parameters of the model that determine the behavior of the scale factor. The proposed model predicts the possibility of eternal cosmic acceleration when <span><math><mrow><mn>0</mn><mo>&lt;</mo><mi>α</mi><mo>&lt;</mo><mn>1</mn><mo>.</mo><mn>19</mn></mrow></math></span>, indicating a continuous expansion of the universe. On the other hand, for <span><math><mrow><mi>α</mi><mo>≥</mo><mn>1</mn><mo>.</mo><mn>19</mn></mrow></math></span>, the model suggests a transition from an early deceleration phase to the current accelerated epoch. This transition aligns with our understanding of the universe’s evolution. Additionally, the model supports the formation of structures in the universe, as it satisfies the Jeans instability condition during the transition from a radiation-dominated era to a matter-dominated era. We focus on analyzing the behavior of the equation of state parameter <span><math><mi>ω</mi></math></span> in our model. We investigate the scalar field and analyze the energy conditions about the obtained solution. To validate our model, we employ various diagnostic tools such as the Jerk, Snap, and Lerk parameters, as well as the Om diagnostic, Velocity of sound, and statefinder diagnostic tools. Additionally, we perform cosmological tests to assess the accuracy of our model. A detailed discussion of the results and the model itself is provided.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92073778","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}