Multiwavelength cluster mass estimates and machine learning

Cohn, J. D.; Battaglia, Nicholas

doi:10.1093/mnras/stz3087

Title: Multiwavelength cluster mass estimates and machine learning

Abstract

ABSTRACT One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is in contrast to finding mass estimates for each observable, normally by using a scaling relation, and then combining these scaling law based mass estimates using a likelihood. We also illustrate how the contributions of each observable to the accuracy of the resulting mass measurement can be compared via model-agnostic Importance Permutation values. Thirdly, as machine learning relies upon the accuracy of the training set in capturing observables, their correlations, and the observational selection function, and as the machine learning training set originates from simulations, two tests of whether a simulation’s correlations are consistent with observations are suggested and explored as well.

Authors:

^[1]; Battaglia, Nicholas ^[2]

Space Sciences Laboratory, University of California, Berkeley, CA 94720, USA, Theoretical Astrophysics Center, University of California, Berkeley, CA 94720, USA
Cornell University, Ithaca, NY 14853, USA

Publication Date:: 2019-11-05

Sponsoring Org.:: USDOE

OSTI Identifier:: 1703310

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: 491 Journal Issue: 2; Journal ID: ISSN 0035-8711

Publisher:: Oxford University Press

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Cohn, J. D., and Battaglia, Nicholas. Multiwavelength cluster mass estimates and machine learning.  United Kingdom: N. p., 2019. 
Web.  doi:10.1093/mnras/stz3087.

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                    Cohn, J. D., & Battaglia, Nicholas. Multiwavelength cluster mass estimates and machine learning.  United Kingdom.  https://doi.org/10.1093/mnras/stz3087

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                    Cohn, J. D., and Battaglia, Nicholas. Tue .  
"Multiwavelength cluster mass estimates and machine learning".  United Kingdom.  https://doi.org/10.1093/mnras/stz3087.

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@article{osti_1703310,

  title        = {Multiwavelength cluster mass estimates and machine learning},

  author       = {Cohn, J. D. and Battaglia, Nicholas},

  abstractNote = {ABSTRACT One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is in contrast to finding mass estimates for each observable, normally by using a scaling relation, and then combining these scaling law based mass estimates using a likelihood. We also illustrate how the contributions of each observable to the accuracy of the resulting mass measurement can be compared via model-agnostic Importance Permutation values. Thirdly, as machine learning relies upon the accuracy of the training set in capturing observables, their correlations, and the observational selection function, and as the machine learning training set originates from simulations, two tests of whether a simulation’s correlations are consistent with observations are suggested and explored as well.},

  doi          = {10.1093/mnras/stz3087},

  journal      = {Monthly Notices of the Royal Astronomical Society},

  number       = 2,

  volume       = 491,

  place        = {United Kingdom},

  year         = {2019},

  month        = {11}

}

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Title: Multiwavelength cluster mass estimates and machine learning

Abstract

Citation Formats

Sunyaev-Zel'dovich fluctuations in the cold dark matter scenario journal, August 1988

An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations journal, January 2019

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Galaxy Groups in the SDSS DR4. I. The Catalog and Basic Properties journal, December 2007

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Cosmological Simulations of Galaxy Clusters journal, February 2011

A Universal Density Profile from Hierarchical Clustering journal, December 1997

Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations journal, October 2019

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Gravitational clustering from scale-free initial conditions journal, December 1988

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Dynamical mass Measurements of Contaminated Galaxy Clusters Using Machine Learning journal, November 2016

Projection effects in galaxy cluster samples: insights from X-ray redshifts journal, June 2019

redMaPPer. I. ALGORITHM AND SDSS DR8 CATALOG journal, April 2014

Painting galaxies into dark matter haloes using machine learning journal, May 2018

The L X –M relation of clusters of galaxies journal, June 2008

The Connection Between Galaxies and Their Dark Matter Halos journal, September 2018

Splashback in accreting dark matter halos journal, November 2014

Cosmological Parameters from Observations of Galaxy Clusters journal, September 2011

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Machine learning and cosmological simulations – II. Hydrodynamical simulations journal, February 2016

What determines satellite galaxy disruption?: What determines satellite galaxy disruption? journal, February 2010

The Mass Function journal, December 2002

Cluster galaxy dynamics and the effects of large-scale environment: Galaxy cluster dynamics journal, August 2010

Mass variance from archival X-ray properties of Dark Energy Survey Year-1 galaxy clusters journal, September 2019

The evolution of substructure - III. The outskirts of clusters journal, February 2005

Planck 2015 results : XIII. Cosmological parameters journal, September 2016

Bias in random forest variable importance measures: Illustrations, sources and a solution journal, January 2007

A Machine Learning Approach for Dynamical mass Measurements of Galaxy Clusters journal, April 2015

A Systematic Analysis of Caustic Methods for Galaxy Cluster Masses journal, August 2013

Tracing cosmic evolution with clusters of galaxies journal, April 2005

The impact of baryons on massive galaxy clusters: halo structure and cluster mass estimates journal, November 2016

machine. journal, October 2001

On the spatial correlations of Abell clusters journal, September 1984

MaxBCG: A Red‐Sequence Galaxy Cluster Finder journal, May 2007

A halo model of galaxy colours and clustering in the Sloan Digital Sky Survey journal, January 2009

Scaling relations for galaxy clusters in the Millennium-XXL simulation: Scaling relations for clusters in the MXXL journal, October 2012

Random Forests journal, January 2001

Conditional variable importance for random forests journal, July 2008

Disentangling correlated scatter in cluster mass measurements: Disentangling correlated scatter in cluster mass measurements journal, October 2012

The evolution of large-scale structure in a universe dominated by cold dark matter journal, May 1985

Covariance in the thermal SZ–weak lensing mass scaling relation of galaxy clusters journal, May 2016

Sunyaev-Zel'dovich fluctuations in the cold dark matter scenario
journal, August 1988

An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations
journal, January 2019

Extremely randomized trees
journal, March 2006

Machine learning and cosmological simulations – I. Semi-analytical models
journal, November 2015

CAIRNS: The Cluster and Infall Region Nearby Survey. I. Redshifts and Mass Profiles
journal, November 2003

Galaxy Groups in the SDSS DR4. I. The Catalog and Basic Properties
journal, December 2007

The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe
journal, January 2017

Cosmological Simulations of Galaxy Clusters
journal, February 2011

A Universal Density Profile from Hierarchical Clustering
journal, December 1997

Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations
journal, October 2019

An analytic model for the spatial clustering of dark matter haloes
journal, September 1996

Gravitational clustering from scale-free initial conditions
journal, December 1988

The Splashback Radius as a Physical halo Boundary and the Growth of halo mass
journal, August 2015

Dynamical mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
journal, November 2016

Projection effects in galaxy cluster samples: insights from X-ray redshifts
journal, June 2019

redMaPPer. I. ALGORITHM AND SDSS DR8 CATALOG
journal, April 2014

Painting galaxies into dark matter haloes using machine learning
journal, May 2018

The L _X –M relation of clusters of galaxies
journal, June 2008

The Connection Between Galaxies and Their Dark Matter Halos
journal, September 2018

Splashback in accreting dark matter halos
journal, November 2014

Cosmological Parameters from Observations of Galaxy Clusters
journal, September 2011

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

Machine learning and cosmological simulations – II. Hydrodynamical simulations
journal, February 2016

What determines satellite galaxy disruption?: What determines satellite galaxy disruption?
journal, February 2010

The Mass Function
journal, December 2002

Cluster galaxy dynamics and the effects of large-scale environment: Galaxy cluster dynamics
journal, August 2010

Mass variance from archival X-ray properties of Dark Energy Survey Year-1 galaxy clusters
journal, September 2019

The evolution of substructure - III. The outskirts of clusters
journal, February 2005

Planck 2015 results : XIII. Cosmological parameters
journal, September 2016

Bias in random forest variable importance measures: Illustrations, sources and a solution
journal, January 2007

A Machine Learning Approach for Dynamical mass Measurements of Galaxy Clusters
journal, April 2015

A Systematic Analysis of Caustic Methods for Galaxy Cluster Masses
journal, August 2013

Tracing cosmic evolution with clusters of galaxies
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The impact of baryons on massive galaxy clusters: halo structure and cluster mass estimates
journal, November 2016

machine.
journal, October 2001

On the spatial correlations of Abell clusters
journal, September 1984

MaxBCG: A Red‐Sequence Galaxy Cluster Finder
journal, May 2007

A halo model of galaxy colours and clustering in the Sloan Digital Sky Survey
journal, January 2009

Scaling relations for galaxy clusters in the Millennium-XXL simulation: Scaling relations for clusters in the MXXL
journal, October 2012

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journal, January 2001

Conditional variable importance for random forests
journal, July 2008

Disentangling correlated scatter in cluster mass measurements: Disentangling correlated scatter in cluster mass measurements
journal, October 2012

The evolution of large-scale structure in a universe dominated by cold dark matter
journal, May 1985

Covariance in the thermal SZ–weak lensing mass scaling relation of galaxy clusters
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