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Title: 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:
ORCiD logo [1];  [2]
  1. Observatório do Valongo, Universidade Federal do Rio de Janeiro, Ladeira do Pedro Antônio 43, Rio de Janeiro RJ 20080-090, Brazil
  2. 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. doi: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. doi: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 = {2020},
month = {2}
}

Journal Article:
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DOI: 10.1093/mnras/staa486

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Works referenced in this record:

The Slope of the Cluster Elliptical Red Sequence: A Probe of Cluster Evolution
journal, July 1998

  • Gladders, Michael D.; Lopez‐Cruz, Omar; Yee, H. K. C.
  • The Astrophysical Journal, Vol. 501, Issue 2
  • DOI: 10.1086/305858

Cosmology and astrophysics from relaxed galaxy clusters – II. Cosmological constraints
journal, April 2014

  • Mantz, A. B.; Allen, S. W.; Morris, R. G.
  • Monthly Notices of the Royal Astronomical Society, Vol. 440, Issue 3
  • DOI: 10.1093/mnras/stu368

SPIDER - III. Environmental dependence of the Fundamental Plane of early-type galaxies: SPIDER - III. Environmental effects on the FP
journal, September 2010

  • La Barbera, F.; Lopes, P. A. A.; De Carvalho, R. R.
  • Monthly Notices of the Royal Astronomical Society, Vol. 408, Issue 3
  • DOI: 10.1111/j.1365-2966.2010.17273.x

The Digitized Second Palomar Observatory Sky Survey (DPOSS). II. Photometric Calibration
journal, December 2004

  • Gal, R. R.; de Carvalho, R. R.; Odewahn, S. C.
  • The Astronomical Journal, Vol. 128, Issue 6
  • DOI: 10.1086/344941

X‐Ray Galaxy Clusters in NoSOCS: Substructure and the Correlation of Optical and X‐Ray Properties
journal, September 2006

  • Lopes, P. A. A.; de Carvalho, R. R.; Capelato, H. V.
  • The Astrophysical Journal, Vol. 648, Issue 1
  • DOI: 10.1086/505630

NoSOCS in SDSS ��� II. Mass calibration of low redshift galaxy clusters with optical and X-ray properties
journal, November 2009

  • Lopes, P. A. A.; de Carvalho, R. R.; Kohl-Moreira, J. L.
  • Monthly Notices of the Royal Astronomical Society, Vol. 399, Issue 4
  • DOI: 10.1111/j.1365-2966.2009.15425.x

NoSOCS in SDSS – VI. The environmental dependence of AGN in clusters and  field in the local Universe
journal, August 2017

  • Lopes, P. A. A.; Ribeiro, A. L. B.; Rembold, S. B.
  • Monthly Notices of the Royal Astronomical Society, Vol. 472, Issue 1
  • DOI: 10.1093/mnras/stx2046

A Spectrophotometric Search for Galaxy Clusters in SDSS
journal, June 2008

  • Yoon, Joo H.; Schawinski, Kevin; Sheen, Yun‐Kyeong
  • The Astrophysical Journal Supplement Series, Vol. 176, Issue 2
  • DOI: 10.1086/528958

The Observational Distribution of Internal Velocity Dispersions in Nearby Galaxy Clusters
journal, December 1996

  • Fadda, D. M. G. F.; Girardi, M.; iuricin, G.
  • The Astrophysical Journal, Vol. 473, Issue 2
  • DOI: 10.1086/178180

NoSOCS in SDSS - I. Sample definition and comparison of mass estimates
journal, January 2009

  • Lopes, P. A. A.; de Carvalho, R. R.; Kohl-Moreira, J. L.
  • Monthly Notices of the Royal Astronomical Society, Vol. 392, Issue 1
  • DOI: 10.1111/j.1365-2966.2008.13962.x

Segregation effects in DEEP2 galaxy groups
journal, September 2016

  • Nascimento, R. S.; Ribeiro, A. L. B.; Lopes, P. A. A.
  • Monthly Notices of the Royal Astronomical Society, Vol. 464, Issue 1
  • DOI: 10.1093/mnras/stw2321

Galaxy morphology in rich clusters - Implications for the formation and evolution of galaxies
journal, March 1980

  • Dressler, A.
  • The Astrophysical Journal, Vol. 236
  • DOI: 10.1086/157753

Galaxies in X-Ray Groups. i. Robust Membership Assignment and the Impact of Group Environments on Quenching
journal, November 2011


The drivers of AGN activity in galaxy clusters: AGN fraction as a function of mass and environment
journal, December 2012

  • Pimbblet, K. A.; Shabala, S. S.; Haines, C. P.
  • Monthly Notices of the Royal Astronomical Society, Vol. 429, Issue 2
  • DOI: 10.1093/mnras/sts470

E/S0 GALAXIES ON THE BLUE COLOR-STELLAR MASS SEQUENCE AT z = 0: FADING MERGERS OR FUTURE SPIRALS?
journal, July 2009

  • Kannappan, Sheila J.; Guie, Jocelly M.; Baker, Andrew J.
  • The Astronomical Journal, Vol. 138, Issue 2
  • DOI: 10.1088/0004-6256/138/2/579

RASS-SDSS galaxy cluster survey: III. Scaling relations of galaxy clusters
journal, March 2005


The Fraction of Cool-core Clusters in X-Ray versus SZ Samples Using Chandra Observations
journal, July 2017

  • Andrade-Santos, Felipe; Jones, Christine; Forman, William R.
  • The Astrophysical Journal, Vol. 843, Issue 1
  • DOI: 10.3847/1538-4357/aa7461

Cosmological Constraints from Galaxy Clusters in the 2500 Square-Degree Spt-Sz Survey
journal, November 2016


Galaxy Cluster Mass Reconstruction Project – II. Quantifying scatter and bias using contrasting mock catalogues
journal, March 2015

  • Old, L.; Wojtak, R.; Mamon, G. A.
  • Monthly Notices of the Royal Astronomical Society, Vol. 449, Issue 2
  • DOI: 10.1093/mnras/stv421

SPIDER – IX. Classifying galaxy groups according to their velocity distribution
journal, July 2013

  • Ribeiro, A. L. B.; de Carvalho, R. R.; Trevisan, M.
  • Monthly Notices of the Royal Astronomical Society, Vol. 434, Issue 1
  • DOI: 10.1093/mnras/stt1071

An automatic taxonomy of galaxy morphology using unsupervised machine learning
journal, September 2017

  • Hocking, Alex; Geach, James E.; Sun, Yi
  • Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 1
  • DOI: 10.1093/mnras/stx2351

Planck 2015 results : XXIV. Cosmology from Sunyaev-Zeldovich cluster counts
journal, September 2016


HICOSMO – cosmology with a complete sample of galaxy clusters – I. Data analysis, sample selection and luminosity–mass scaling relation
journal, April 2017

  • Schellenberger, G.; Reiprich, T. H.
  • Monthly Notices of the Royal Astronomical Society, Vol. 469, Issue 3
  • DOI: 10.1093/mnras/stx1022

Optical substructure and BCG offsets of Sunyaev–Zel’dovich and X-ray-selected galaxy clusters
journal, May 2018

  • Lopes, Paulo A. A.; Trevisan, M.; Laganá, T. F.
  • Monthly Notices of the Royal Astronomical Society, Vol. 478, Issue 4
  • DOI: 10.1093/mnras/sty1374

CIRS: Cluster Infall Regions in the Sloan Digital Sky Survey. I. Infall Patterns and Mass Profiles
journal, August 2006

  • Rines, Kenneth; Diaferio, Antonaldo
  • The Astronomical Journal, Vol. 132, Issue 3
  • DOI: 10.1086/506017

RASS-SDSS Galaxy cluster survey: IV. A ubiquitous dwarf galaxy population in clusters
journal, December 2005


Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data
journal, October 2001

  • Strateva, Iskra; Ivezić, Željko; Knapp, Gillian R.
  • The Astronomical Journal, Vol. 122, Issue 4
  • DOI: 10.1086/323301

AMICO: optimized detection of galaxy clusters in photometric surveys
journal, October 2017

  • Bellagamba, Fabio; Roncarelli, Mauro; Maturi, Matteo
  • Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 4
  • DOI: 10.1093/mnras/stx2701

TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests
journal, May 2013

  • Carrasco Kind, Matias; Brunner, Robert J.
  • Monthly Notices of the Royal Astronomical Society, Vol. 432, Issue 2
  • DOI: 10.1093/mnras/stt574

A Comparison of Galaxy Counting Techniques in Spectroscopically Undersampled Regions
journal, October 2016


A finer view of the conditional galaxy luminosity function and magnitude-gap statistics
journal, July 2017

  • Trevisan, M.; Mamon, G. A.
  • Monthly Notices of the Royal Astronomical Society, Vol. 471, Issue 2
  • DOI: 10.1093/mnras/stx1656

redMaPPer – IV. Photometric membership identification of red cluster galaxies with 1 per cent precision
journal, August 2015

  • Rozo, E.; Rykoff, E. S.; Becker, M.
  • Monthly Notices of the Royal Astronomical Society, Vol. 453, Issue 1
  • DOI: 10.1093/mnras/stv1560

The Eighth data Release of the Sloan Digital sky Survey: First data from Sdss-Iii
journal, March 2011

  • Aihara, Hiroaki; Allende Prieto, Carlos; An, Deokkeun
  • The Astrophysical Journal Supplement Series, Vol. 193, Issue 2
  • DOI: 10.1088/0067-0049/193/2/29

Empirical photometric redshifts of luminous red galaxies and clusters in the Sloan Digital Sky Survey
journal, September 2007


Robust Machine Learning Applied to Astronomical Data Sets. II. Quantifying Photometric Redshifts for Quasars Using Instance‐based Learning
journal, July 2007

  • Ball, Nicholas M.; Brunner, Robert J.; Myers, Adam D.
  • The Astrophysical Journal, Vol. 663, Issue 2
  • DOI: 10.1086/518362

The Northern Sky Optical Cluster Survey. IV. An Intermediate-Redshift Galaxy Cluster Catalog and the Comparison of Two Detection Algorithms
journal, September 2004

  • Lopes, P. A. A.; de Carvalho, R. R.; Gal, R. R.
  • The Astronomical Journal, Vol. 128, Issue 3
  • DOI: 10.1086/423038

NoSOCS in SDSS – V. Red disc and blue bulge galaxies across different environments
journal, June 2016

  • Lopes, P. A. A.; Rembold, S. B.; Ribeiro, A. L. B.
  • Monthly Notices of the Royal Astronomical Society, Vol. 461, Issue 3
  • DOI: 10.1093/mnras/stw1497

NoSOCS in SDSS: III. The interplay between galaxy evolution and the dynamical state of galaxy clusters
journal, July 2013


Measuring Omega0 using cluster evolution
journal, August 1998


UPMASK: unsupervised photometric membership assignment in stellar clusters
journal, December 2013


Percolation Galaxy Groups and Clusters in the SDSS Redshift Survey: Identification, Catalogs, and the Multiplicity Function
journal, November 2006

  • Berlind, Andreas A.; Frieman, Joshua; Weinberg, David H.
  • The Astrophysical Journal Supplement Series, Vol. 167, Issue 1
  • DOI: 10.1086/508170

How special are brightest group and cluster galaxies?
journal, July 2007

  • Von Der Linden, Anja; Best, Philip N.; Kauffmann, Guinevere
  • Monthly Notices of the Royal Astronomical Society, Vol. 379, Issue 3
  • DOI: 10.1111/j.1365-2966.2007.11940.x

The Cluster Mass Function from Early Sloan Digital Sky Survey Data: Cosmological Implications
journal, March 2003

  • Bahcall, Neta A.; Dong, Feng; Bode, Paul
  • The Astrophysical Journal, Vol. 585, Issue 1
  • DOI: 10.1086/345981

A Quantitative Measure of the Richness of Galaxy Clusters
journal, May 1999

  • Yee, H. K. C.; López-Cruz, Omar
  • The Astronomical Journal, Vol. 117, Issue 5
  • DOI: 10.1086/300837

A new method to assign galaxy cluster membership using photometric redshifts
journal, November 2016


NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters
journal, November 2013

  • Lopes, P. A. A.; Ribeiro, A. L. B.; Rembold, S. B.
  • Monthly Notices of the Royal Astronomical Society, Vol. 437, Issue 3
  • DOI: 10.1093/mnras/stt2064

A Probabilistic Quantification of Galaxy Cluster Membership
journal, December 2000

  • Brunner, R. J.; Lubin, L. M.
  • The Astronomical Journal, Vol. 120, Issue 6
  • DOI: 10.1086/316849

Photometric redshifts for the SDSS Data Release 12
journal, April 2016

  • Beck, Róbert; Dobos, László; Budavári, Tamás
  • Monthly Notices of the Royal Astronomical Society, Vol. 460, Issue 2
  • DOI: 10.1093/mnras/stw1009

Machine learning on difference image analysis: A comparison of methods for transient detection
journal, July 2019


A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images: I. Method description
journal, November 2007


The Digitized Second Palomar Observatory Sky Survey (DPOSS). III. Star-Galaxy Separation
journal, December 2004

  • Odewahn, S. C.; de Carvalho, R. R.; Gal, R. R.
  • The Astronomical Journal, Vol. 128, Issue 6
  • DOI: 10.1086/425525

The Northern sky Optical Cluster Survey. iii. a Cluster Catalog Covering pi Steradians
journal, January 2009


Weak lensing and spectroscopic analysis of the nearby dissociative merging galaxy cluster Abell 3376
journal, April 2017

  • Monteiro-Oliveira, R.; Lima Neto, G. B.; Cypriano, E. S.
  • Monthly Notices of the Royal Astronomical Society, Vol. 468, Issue 4
  • DOI: 10.1093/mnras/stx791

The Northern Sky Optical Cluster Survey. II. An Objective Cluster Catalog for 5800 Square Degrees
journal, April 2003

  • Gal, R. R.; de Carvalho, R. R.; Lopes, P. A. A.
  • The Astronomical Journal, Vol. 125, Issue 4
  • DOI: 10.1086/368240

A hybrid ensemble learning approach to star–galaxy classification
journal, August 2015

  • Kim, Edward J.; Brunner, Robert J.; Carrasco Kind, Matias
  • Monthly Notices of the Royal Astronomical Society, Vol. 453, Issue 1
  • DOI: 10.1093/mnras/stv1608

The Color‐Magnitude Effect in Early‐Type Cluster Galaxies
journal, October 2004

  • Lopez‐Cruz, Omar; Barkhouse, Wayne A.; Yee, H. K. C.
  • The Astrophysical Journal, Vol. 614, Issue 2
  • DOI: 10.1086/423664