Astronomy and Computing最新文献_第9页

GEPINN: An innovative hybrid method for a symbolic solution to the Lane–Emden type equation based on grammatical evolution and physics-informed neural networks GEPINN：基于语法进化和物理信息神经网络的创新型混合方法，用于符号解Lane-Emden型方程

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-28 DOI: 10.1016/j.ascom.2024.100846

Hassan Dana Mazraeh , Kourosh Parand

引用次数: 0

Surveying image segmentation approaches in astronomy 天文学图像分割方法概览

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-23 DOI: 10.1016/j.ascom.2024.100838

D. Xu , Y. Zhu

{"title":"Surveying image segmentation approaches in astronomy","authors":"D. Xu , Y. Zhu","doi":"10.1016/j.ascom.2024.100838","DOIUrl":"10.1016/j.ascom.2024.100838","url":null,"abstract":"<div><p>Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100838"},"PeriodicalIF":2.5,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132980","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}

引用次数: 0

Reinforcement learning 强化学习

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-21 DOI: 10.1016/j.ascom.2024.100833

S. Yatawatta

引用次数: 0

Learning from the present for the future: The Jülich LOFAR Long-term Archive 立足现在，放眼未来：尤利希 LOFAR 长期档案

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-20 DOI: 10.1016/j.ascom.2024.100835

C. Manzano, A. Miskolczi, H. Stiele, V. Vybornov, T. Fieseler, S. Pfalzner

{"title":"Learning from the present for the future: The Jülich LOFAR Long-term Archive","authors":"C. Manzano, A. Miskolczi, H. Stiele, V. Vybornov, T. Fieseler, S. Pfalzner","doi":"10.1016/j.ascom.2024.100835","DOIUrl":"10.1016/j.ascom.2024.100835","url":null,"abstract":"<div><p>The Forschungszentrum Jülich has been hosting the German part of the LOFAR archive since 2013. It is Germany’s most extensive radio astronomy archive, currently storing nearly 22 petabytes (PB) of data. Future radio telescopes are expected to require a dramatic increase in long-term data storage. Here, we take stock of the current data management of the Jülich LOFAR Data Archive, describe the ingestion, the storage system, the export to the long-term archive, and the request chain. We analysed the data availability over the last 10 years and searched for the underlying data access pattern and the energy consumption of the process. We determine hardware-related limiting factors, such as network bandwidth and cache pool availability and performance, and software aspects, e.g. workflow adjustment and parameter tuning, as the main data storage bottlenecks. By contrast, the challenge in providing the data from the archive for the users lies in retrieving the data from the tape archive and staging them. Building on this analysis, we suggest how to avoid/mitigate these problems in the future and define the requirements for future even more extensive long-term data archives.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100835"},"PeriodicalIF":2.5,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000507/pdfft?md5=8384bf7573be7dd5e41b8607f6174d14&pid=1-s2.0-S2213133724000507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141133607","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}

引用次数: 0

CNN-based track reconstruction study for gamma-ray pair telescope 基于 CNN 的伽马射线对望远镜轨迹重建研究

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-19 DOI: 10.1016/j.ascom.2024.100834

L. Yu , J. Wang , D. Guo , W. Peng , R. Qiao , K. Gong , Y. Liu , J. Wang , C. Zhang , W. Zhang

引用次数: 0

On the equilibrium points of the collinear restricted 4-body problem with non-spherical bodies 关于非球体的共线受限四体问题的平衡点

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-16 DOI: 10.1016/j.ascom.2024.100832

H.I. Alrebdi , K.S. Al-mugren , F.L. Dubeibe , M.S. Suraj , E.E. Zotos

引用次数: 0

A study of the equilibrium dynamics of the test particle in the collinear circular restricted four-body problem with non-spherical central primary 具有非球形中心原初性的共线圆形受限四体问题中测试粒子的平衡动力学研究

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-05-16 DOI: 10.1016/j.ascom.2024.100831

M.S. Suraj , M. Bhushan , M.C. Asique

{"title":"A study of the equilibrium dynamics of the test particle in the collinear circular restricted four-body problem with non-spherical central primary","authors":"M.S. Suraj , M. Bhushan , M.C. Asique","doi":"10.1016/j.ascom.2024.100831","DOIUrl":"10.1016/j.ascom.2024.100831","url":null,"abstract":"<div><p>We consider the collinear restricted four-body problem (CR4BP), where the test particle of infinitesimal mass is moving under the gravitational influence of the three primary bodies. It is further assumed that the central primary is a non-spherical body, particularly either an oblate or prolate spheroid, whereas the peripheral primaries are spherical in shape. A numerical analysis is presented to unveil the effect of the oblateness and prolateness parameters on the position of equilibrium points (EPs) and their linear stability in the CR4BP. Moreover, the permissible regions of possible motion as determined by the zero-velocity surface and associated equipotential curves and the basins of convergence linked with the EPs on the orbital plane are presented. The existence and number of collinear EPs and non-collinear EPs in the problem depend on the combination of the mass parameter of the primaries and the oblateness/prolateness parameter. Additionally, the application of the problem in the Saturn-Moon(1)-Moon(2)-System has been presented.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100831"},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024137","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}

引用次数: 0

Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks 利用紫外线至亚毫米光度测量和深度生成网络重构星系的中红外光谱

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-04-01 DOI: 10.1016/j.ascom.2024.100823

Agapi Rissaki , O. Pavlou , D. Fotakis , V. Papadopoulou Lesta , A. Efstathiou

{"title":"Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks","authors":"Agapi Rissaki , O. Pavlou , D. Fotakis , V. Papadopoulou Lesta , A. Efstathiou","doi":"10.1016/j.ascom.2024.100823","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100823","url":null,"abstract":"<div><p>The mid-infrared spectra of galaxies are rich in features such as the Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give valuable information about the physics of galaxies and their evolution. For example they can provide information about the relative contribution of star formation and accretion from a supermassive black hole to the power output of galaxies. However, the mid-infrared spectra are currently available for a very small fraction of galaxies that have been detected in deep multi-wavelength surveys of the sky. In this paper we explore whether Deep Generative Network methods can be used to reconstruct mid-infrared spectra in the 5–35<span><math><mi>μ</mi></math></span>m range using the limited multi-wavelength photometry in <span><math><mrow><mo>∼</mo><mn>20</mn></mrow></math></span> bands from the ultraviolet to the submillimeter which is typically available in extragalactic surveys. For this purpose we use simulated spectra computed with a combination of radiative transfer models for starbursts, active galactic nucleus (AGN) tori and host galaxies. We find that our method using Deep Generative Networks, namely Generative Adversarial Networks and Generative Latent Optimization models, can efficiently produce high quality reconstructions of mid-infrared spectra in <span><math><mo>∼</mo></math></span> 60% of the cases. We discuss how our method can be improved by using more training data, photometric bands, model parameters or by employing other generative networks.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100823"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604964","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}

引用次数: 0

Membership determination in open clusters using the DBSCAN Clustering Algorithm 使用 DBSCAN 聚类算法确定开放聚类的成员资格

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-04-01 DOI: 10.1016/j.ascom.2024.100826

M. Raja , P. Hasan , Md. Mahmudunnobe , Md. Saifuddin , S.N. Hasan

引用次数: 0

Machine Learning methods in Astronomy 天文学中的机器学习方法

IF 2.5 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-04-01 DOI: 10.1016/j.ascom.2024.100830

Maggie Lieu , Ting-Yun Cheng

引用次数: 0