D. Farid , H. Aung , D. Nagai , A. Farahi , E. Rozo
{"title":"C\n2-GaMe: Classification of cluster galaxy membership with machine learning","authors":"D. Farid , H. Aung , D. Nagai , A. Farahi , E. Rozo","doi":"10.1016/j.ascom.2023.100743","DOIUrl":null,"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>\n<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>\n<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>\n<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><</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":1.9000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Astronomy and Computing","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213133723000586","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
We present Classification of Cluster Galaxy Members (C
-GaMe), a classification algorithm 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 C
-GaMe 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 C
-GaMe can recover the distribution of orbiting and infalling galaxies’ position and velocity distribution with 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.
Astronomy and ComputingASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍:
Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.