Astronomy and Computing最新文献

{"title":"Exploring celestial classification: Astrophysical features-guided machine learning for spectral and morphological analysis","authors":"Md. Fairuz Siddiquee ,&nbsp;Md Mehedi Hasan ,&nbsp;Shifat E. Arman ,&nbsp;Md. Shahedul Islam ,&nbsp;AKM Azad","doi":"10.1016/j.ascom.2025.101048","DOIUrl":"10.1016/j.ascom.2025.101048","url":null,"abstract":"<div><div>Celestial classification, traditionally based on spectral analysis, helps understand the characteristics and distribution of solar radiation, aiding in the design of solar sail technology and potentially reducing energy costs in space missions. This research investigates the spectral and morphological classification of celestial entities by integrating feature engineering with astrophysical knowledge and principles, utilizing Machine Learning (ML) methodologies. These insights enabled the careful enhancement of the feature set, resulting in the systematic elimination of irrelevant and unstructured data, thereby improving both the model’s accuracy and its computing efficiency. The examination of the Sloan Digital Sky Survey (SDSS) dataset highlights redshift and near-infrared measurements (i and z filters) as crucial spectral parameters for classifying stars, galaxies, and quasars. Feature selection streamlined the dataset from 17 initial features to the most pertinent filters (u, g, r, i, z) and redshift, thereby enhancing computational efficiency and model correctness. The Random Forest classifier attained the best accuracy (98%) across all classes by utilizing these features, surpassing both k-nearest neighbors (k-NN) and support vector machines (SVM). For morphological classification, the YOLOv5, YOLOv7, and YOLOv8 models were trained on a tailored dataset to classify galaxies into five morphological categories: Elliptical, Spiral, Irregular, Merging, and Peculiar. Quantitative research indicated that YOLOv8 achieved the highest performance, with 95.5% precision across all galaxy classifications and an overall recall of 73.7%, underscoring its effectiveness in identifying various galaxy morphologies. This comprehensive investigation enhances model interpretability and accuracy, underscores the efficacy of astrophysically motivated features, and establishes a robust framework for real-time large data analysis in astrophysical research, providing a benchmark for industrial applications through advanced data-driven approaches.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101048"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883942","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":"SPAN: A cross-platform Python GUI software for optical and near-infrared spectral analysis","authors":"D. Gasparri ,&nbsp;L. Morelli ,&nbsp;U. Battino ,&nbsp;J. Méndez-Abreu ,&nbsp;A. de Lorenzo-Cáceres","doi":"10.1016/j.ascom.2025.101051","DOIUrl":"10.1016/j.ascom.2025.101051","url":null,"abstract":"<div><div>The increasing availability of high-quality optical and near-infrared spectroscopic data, as well as advances in modelling techniques, have greatly expanded the scientific potential of spectroscopic studies. However, the software tools needed to exploit this potential often remain fragmented across multiple specialised packages, requiring scripting skills and manual integration to handle complex workflows. In this paper we present <span>SPAN</span> (SPectral ANalysis), a cross-platform, Python-based Graphical User Interface (GUI) software that integrates the essential steps of the modern spectroscopic workflow within a single, user-friendly environment. SPAN provides a coherent framework that unifies data preparation, spectral processing, and analysis tasks, using the pPXF software as its core engine for full spectral fitting. SPAN allows users to extract 1D spectra from FITS images and datacubes, perform spectral processing (e.g. Doppler correction, continuum modelling, denoising), and carry out detailed analyses, including equivalent width measurements, stellar and gas kinematics, and stellar population studies. It runs natively on Windows, Linux, macOS, and Android, and is fully task-driven, requiring no prior coding experience. We validate SPAN by comparing its output with existing pipelines and literature studies. By offering a flexible, accessible, and well integrated environment, SPAN simplifies and accelerates the spectral analysis workflow, while maintaining scientific accuracy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101051"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790289","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":"Search for the best diagnostics in globular clusters (MRSES Approach)","authors":"A. Chilingarian","doi":"10.1016/j.ascom.2025.101047","DOIUrl":"10.1016/j.ascom.2025.101047","url":null,"abstract":"<div><div>High-dimensional classification and feature extraction present significant challenges in analyzing astrophysical data. This paper describes the implementation of the Multiple Random Search with Early Stop (MRSES) algorithm to detect weak, structured signals within high-dimensional noise. We adapt MRSES for use in stellar population studies by applying it to APOGEE-derived elemental abundance data from the globular clusters M13 and M3.</div><div>The MRSES method uses stochastic subset evaluation guided by the Bhattacharyya distance to rank features by their contribution to class separability. In globular clusters, this approach enables the recovery of chemically distinct subpopulations without assuming linearity or relying on marginal statistics. In M13, MRSES identifies classic second-generation markers, such as [Al/Fe] and [Na/Fe]. Meanwhile, in M3, it detects more subtle variations driven by iron-group elements, highlighting its sensitivity to weak internal differences.</div><div>Benchmark tests on synthetic Gaussian datasets verify the method’s robustness under different dimensionalities and correlation structures. MRSES avoids classical overfitting by using stochastic sampling instead of parametric fitting, and the Bhattacharyya distance threshold reflects an empirically calibrated noise boundary rather than an arbitrary parameter.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101047"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790291","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":"Mercury-Opal: The GPU-accelerated version of the n-body code for planet formation Mercury-Arχes","authors":"Paolo Matteo Simonetti ,&nbsp;Diego Turrini ,&nbsp;Romolo Politi ,&nbsp;Scigé J. Liu ,&nbsp;Sergio Fonte ,&nbsp;Danae Polychroni ,&nbsp;Stavro Lambrov Ivanovski","doi":"10.1016/j.ascom.2026.101062","DOIUrl":"10.1016/j.ascom.2026.101062","url":null,"abstract":"<div><div>Large n-body simulations with fully interacting objects represent the next frontier in computational planetary formation studies. In this paper, we present <span>Mercury-Opal</span>, the GPU-accelerated version of the n-body planet formation code <span>Mercury-Ar</span> <span><math><mrow><mspace></mspace><mi>χ</mi><mspace></mspace></mrow></math></span> <span>es</span>. The porting to GPU computing has been performed through OpenACC to ensure cross-platform support and minimize the code restructuring efforts while retaining most of the performance increase expected from GPU computing. We tested <span>Mercury-Opal</span> <!--> <!-->against its parent code <span>Mercury-Ar</span> <span><math><mrow><mspace></mspace><mi>χ</mi><mspace></mspace></mrow></math></span> <span>es</span> <!--> <!-->under conditions that put GPU computing at disadvantage and nevertheless show how the GPU-based execution provides advantages with respect to CPU-serial execution even for limited computational loads.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101062"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077695","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 PLUTO code on GPUs: A first look at Eulerian MHD methods","authors":"M. Rossazza ,&nbsp;A. Mignone ,&nbsp;M. Bugli ,&nbsp;S. Truzzi ,&nbsp;L. Riha ,&nbsp;T. Panoc ,&nbsp;O. Vysocky ,&nbsp;N. Shukla ,&nbsp;A. Romeo ,&nbsp;V. Berta","doi":"10.1016/j.ascom.2026.101076","DOIUrl":"10.1016/j.ascom.2026.101076","url":null,"abstract":"<div><div>We present preliminary performance results of <span>gPLUTO</span>, the new GPU-optimized implementation of the <span>PLUTO</span> code for computational plasma astrophysics. Like its predecessor, <span>gPLUTO</span> employs a Eulerian finite-volume formulation to numerically solve the equations of magnetohydrodynamics (MHD) in multiple spatial dimensions. Still, this new implementation is a complete rewrite in C++ and leverages the OpenACC programming model to achieve acceleration on NVIDIA GPUs. While a more comprehensive description of the code and its several other modules will be presented in a future paper, here we focus on some preparatory results that demonstrate the code potential and performance on pre exa-scale parallel architectures.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101076"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173401","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":"Accelerating radio astronomy imaging with RICK: A step towards SKA-Mid and SKA-Low","authors":"G. Lacopo ,&nbsp;E. De Rubeis ,&nbsp;C. Gheller ,&nbsp;G. Taffoni ,&nbsp;L. Tornatore","doi":"10.1016/j.ascom.2026.101074","DOIUrl":"10.1016/j.ascom.2026.101074","url":null,"abstract":"<div><div>The data volumes generated by modern radio interferometers, such as the SKA precursors, present significant computational challenges for imaging pipelines. Addressing the need for high-performance, portable, and scalable software, we present <span>RICK</span> 2.0 (Radio Imaging Code Kernels). This work introduces a novel implementation that leverages the HeFFTe library for distributed Fast Fourier Transforms, ensuring portability across diverse HPC architectures, including multi-core CPUs and accelerators. We validate <span>RICK</span>’s correctness and performance against real observational data from both MeerKAT and LOFAR. Our results demonstrate that the HeFFTe-based implementation offers substantial performance advantages, particularly when running on GPUs, and scales effectively with large pixel resolutions and a high number of frequency planes. This new architecture overcomes the critical scaling limitations identified in previous work (Paper II, Paper III), where communication overheads consumed up to 96% of the runtime due to the necessity of communicating the entire grid. This new <span>RICK</span> version drastically reduces this communication impact, representing a scalable and efficient imaging solution ready for the SKA era.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101074"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173402","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":"Automated lunar crescent extraction from astronomical imaging using python-based vision algorithms","authors":"Wan Aiman Hakimie Wan Abdul Hadi ,&nbsp;Muhamad Syazwan Faid ,&nbsp;Mohd Saiful Anwar Mohd Nawawi ,&nbsp;Raihana Abdul Wahab ,&nbsp;Nazhatulshima Ahmad ,&nbsp;Mohd Zambri Zainuddin ,&nbsp;Ahmad Adib Rofiuddin ,&nbsp;Muhammad Ridzuan Hashim","doi":"10.1016/j.ascom.2025.101057","DOIUrl":"10.1016/j.ascom.2025.101057","url":null,"abstract":"<div><div>The detection of the lunar crescent is a fundamental challenge in observational astronomy, particularly in the context of time-sensitive astronomical phenomena. This study presents a computational approach for automated lunar crescent extraction from astronomical images using Python-based vision algorithms. While previous efforts in this domain have employed image processing techniques, they were often constrained by dataset bias and limited empirical testing on real-world imagery. In this work, a total of 67 observational lunar images from the Optical Astronomy Research Laboratory (OpARL), spanning 2000 to 2025, were analysed using a sequence of digital image processing techniques including grayscale masking, Gaussian filtering, edge detection, contour enhancement, and object recognition. The approach achieved a detection success rate of 70.15% in predicting a lunar crescent appearance in an imaging. The result also finds correlations between detection outcomes and lunar altitude and elongation. The findings demonstrate the effectiveness of integrating classical image processing pipelines with astronomical datasets for reliable crescent identification. This improves the process of identification of an appearance of a lunar crescent image during live observations or post processing.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101057"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925360","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":"Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model","authors":"F. Incardona ,&nbsp;A. Costa ,&nbsp;F. Farsian ,&nbsp;F. Franchina ,&nbsp;G. Leto ,&nbsp;E. Mastriani ,&nbsp;K. Munari ,&nbsp;G. Pareschi ,&nbsp;S. Scuderi ,&nbsp;S. Spinello ,&nbsp;G. Tosti ,&nbsp;ASTRI Project","doi":"10.1016/j.ascom.2026.101071","DOIUrl":"10.1016/j.ascom.2026.101071","url":null,"abstract":"<div><div>This study presents a Normal Behavior Model (NBM) developed to forecast monitoring time-series data from the ASTRI-Horn Cherenkov telescope under normal operating conditions. The analysis focused on 15 physical variables acquired by the Telescope Control Unit between September 2022 and July 2024, representing sensor measurements from the Azimuth and Elevation motors. After data cleaning, resampling, feature selection, and correlation analysis, the dataset was segmented into fixed-length intervals, in which the first <span><math><mi>I</mi></math></span> samples represented the input sequence provided to the model, while the forecast length, <span><math><mi>T</mi></math></span>, indicated the number of future time steps to be predicted. A sliding-window technique was then applied to increase the number of intervals. A Multi-Layer Perceptron (MLP) was trained to perform multivariate forecasting across all features simultaneously. Model performance was evaluated using the Mean Squared Error (MSE) and the Normalized Median Absolute Deviation (NMAD), and it was also benchmarked against a Long Short-Term Memory (LSTM) network. The MLP model demonstrated consistent results across different features and <span><math><mi>I</mi></math></span>–<span><math><mi>T</mi></math></span> configurations, and matched the performance of the LSTM while converging faster. It achieved an MSE of <span><math><mrow><mn>0</mn><mo>.</mo><mn>019</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>003</mn></mrow></math></span> and an NMAD of <span><math><mrow><mn>0</mn><mo>.</mo><mn>032</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>009</mn></mrow></math></span> on the test set under its best configuration (4 hidden layers, 720 units per layer, and <span><math><mi>I</mi></math></span>–<span><math><mi>T</mi></math></span> lengths of 300 samples each, corresponding to 5 h at 1-minute resolution). Extending the forecast horizon up to 6.5 h—the maximum allowed by this configuration—did not degrade performance, confirming the model’s effectiveness in providing reliable hour-scale predictions. The proposed NBM provides a powerful tool for enabling early anomaly detection in online ASTRI-Horn monitoring time series, offering a basis for the future development of a prognostics and health management system that supports predictive maintenance.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101071"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077697","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":"Deep learning approach for automated detection of space objects in astronomical imaging","authors":"Cristian Omat ,&nbsp;Mirel Birlan ,&nbsp;Dan Alin Nedelcu ,&nbsp;Simon Anghel","doi":"10.1016/j.ascom.2026.101081","DOIUrl":"10.1016/j.ascom.2026.101081","url":null,"abstract":"<div><div>The exponential growth in low Earth orbit (LEO) satellite deployments over recent years has introduced substantial challenges for ground-based astronomical observations. Astronomers now routinely encounter satellite trails and space debris crossing their telescope fields of view, necessitating time-intensive manual inspection to identify usable frames for scientific analysis. Simultaneously, detecting and cataloging these objects is essential for building and maintaining Resident Space Object (RSO) catalogs, which support safe and sustainable space operations. This work develops and evaluates a deep learning approach based on <span>YOLOv12</span> to automatically detect satellite and debris trails in all-sky astronomical images. We build a labeled dataset of 7794 FITS images, derived from 8596 raw observations collected at the Berthelot Observatory between January 24 and March 23, 2025 and expand it through augmentation to 13,162 images for training, validation, and testing. Our trained models achieve high detection performance on held-out test data while remaining computationally efficient for real-time use. The trained models demonstrate robust performance across different observational conditions, providing an efficient tool for both protecting astronomical data quality and supporting Space Situational Awareness through the discovery of previously unknown objects and monitoring of cataloged satellites within all-sky monitoring networks.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101081"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173467","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":"Speeding Up the GUIBRUSH® retrieval code for modelling exoplanetary atmospheres","authors":"G. Guilluy ,&nbsp;P. Giacobbe ,&nbsp;F. Amadori ,&nbsp;G. Quaglia ,&nbsp;A.S. Bonomo","doi":"10.1016/j.ascom.2025.101055","DOIUrl":"10.1016/j.ascom.2025.101055","url":null,"abstract":"<div><div>Spectral retrieval is a fundamental tool for investigating the chemical composition and physical properties of exoplanetary atmospheres. These retrievals rely on reconstructing the path of photons from the host star through the exoplanet’s atmosphere, a task typically accomplished by solving the radiative transfer (RT) equations. This calculation constitutes one of the main computational bottlenecks of retrievals. Another significant bottleneck arises from the need to generate a very large number of models and compare them with observations in order to explore the parameter space within a Bayesian framework. In this work, we focused on improving both of these critical bottlenecks within our framework GUIBRUSH® (Graphic User Interface for Bayesian Retrieval Using High Resolution Spectroscopy). First, we optimised the efficiency of our Bayesian analysis tool, parallelising both forward-model computation and its comparison with observational data. This strategy yielded a performance improvement of approximately <span><math><mrow><mo>∼</mo><mn>10</mn><mo>×</mo></mrow></math></span> relative to the original implementation. Secondly, we accelerated the RT calculation. We first benchmarked the performance of two widely adopted Python-based packages, <span>petitRADTRANS</span> and <span>PyratBay</span>, on the hot Jupiter WASP-127b. We found that in the spectral band we investigated (namely, 0.95–<span><math><mrow><mn>2</mn><mo>.</mo><mn>45</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> in the near-infrared), <span>PyratBay</span> ran approximately twice as fast as <span>petitRADTRANS</span> on CPU, motivating its adoption as the baseline for further optimisation. We then implemented a GPU-accelerated version of <span>PyratBay</span> by parallelising the computation of the optical depth and transmission spectrum across the wavelength domain using <span>PyCUDA</span>, which provides a seamless interface between Python and NVIDIA’s <span>CUDA</span> framework. When computing 100 models, the GPU implementation of <span>PyratBay</span> achieved a median speed-up of approximately <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>×</mo></mrow></math></span> per model compared to the CPU version. To extend this gain to full retrievals, we integrated the GPU version with Python’s <span>multiprocessing-pool</span>, enabling large model grids to be evaluated in parallel. For our test case on WASP-127<!--> <!-->b, the total runtime to compute 99123 models (corresponding to the number of iterations required for the retrieval to converge) was reduced from 173082.4 s to 10046.43 s.</div><div>We are now working on integrating the GPU-accelerated <span>PyratBay</span> version directly into GUIBRUSH®, enabling fully GPU-powered atmospheric retrievals.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101055"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925358","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}