Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe
Abstract
ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $$M_r^* + 3$$, we limit our work to 30 clusters with $$z$$phot-cl ≤ 0.045. Masses (M200) are larger than $$\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$$ (most above $$3\times 10^{14} \, \mathrm{M}_{\odot }$$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $$\sim 92{\rm{ per\ cent}}$$ and purity, P $$\sim 87{\rm{ per\ cent}}$$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.
- Authors:
-
[1];
- Observatório do Valongo, Universidade Federal do Rio de Janeiro, Ladeira do Pedro Antônio 43, Rio de Janeiro RJ 20080-090, Brazil
- Laboratório de Astrofísica Teórica e Observacional – Departamento de Ciências Exatas e Tecnológicas –Universidade Estadual de Santa Cruz, 45650-000 Ilhéus, BA, Brazil
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1604118
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 493 Journal Issue: 3; Journal ID: ISSN 0035-8711
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Lopes, Paulo A. A., and Ribeiro, André L. B. Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe. United Kingdom: N. p., 2020.
Web. doi:10.1093/mnras/staa486.
Lopes, Paulo A. A., & Ribeiro, André L. B. Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe. United Kingdom. https://doi.org/10.1093/mnras/staa486
Lopes, Paulo A. A., and Ribeiro, André L. B. Wed .
"Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe". United Kingdom. https://doi.org/10.1093/mnras/staa486.
@article{osti_1604118,
title = {Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe},
author = {Lopes, Paulo A. A. and Ribeiro, André L. B.},
abstractNote = {ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.},
doi = {10.1093/mnras/staa486},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 493,
place = {United Kingdom},
year = {Wed Feb 19 00:00:00 EST 2020},
month = {Wed Feb 19 00:00:00 EST 2020}
}
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