Multiwavelength cluster mass estimates and machine learning

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.

arXiv comment12 pages, final version to appear in MNRAS. Expanded explanations, and added examples

References in corpus (36)

• Scikit-learn: Machine Learning in Python
• Planck 2015 results. XIII. Cosmological parameters
• A Universal Density Profile from Hierarchical Clustering
• An analytic model for the spatial clustering of dark matter haloes
• Cosmological Parameters from Observations of Galaxy Clusters
• Galaxy Groups in the SDSS DR4: I. The Catalogue and Basic Properties
• Tracing cosmic evolution with clusters of galaxies
• The Connection between Galaxies and their Dark Matter Halos
• redMaPPer I: Algorithm and SDSS DR8 Catalog
• Scaling relations for galaxy clusters in the Millennium-XXL simulation
• The splashback radius as a physical halo boundary and the growth of halo mass
• The evolution of substructure III: the outskirts of clusters
• CAIRNS: The Cluster And Infall Region Nearby Survey I. Redshifts and Mass Profiles
• MaxBCG: A Red Sequence Galaxy Cluster Finder
• The mass function
• Splashback in accreting dark matter halos
• Cosmological simulations of galaxy clusters
• A Halo Model of Galaxy Colors and Clustering in the SDSS
• What determines satellite galaxy disruption?
• Cluster Galaxy Dynamics and the Effects of Large Scale Environment
• The L_X--M relation of Clusters of Galaxies
• The impact of baryons on massive galaxy clusters: halo structure and cluster mass estimates
• Galaxy Cluster Mass Reconstruction Project: II. Quantifying scatter and bias using contrasting mock catalogues
• A Systematic Analysis of Caustic Methods for Galaxy Cluster Masses
• A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
• Painting galaxies into dark matter halos using machine learning
• Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations
• Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
• Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations
• Machine Learning and Cosmological Simulations I: Semi-Analytical Models
• An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations
• Disentangling correlated scatter in cluster mass measurements
• Mass Variance from Archival X-ray Properties of Dark Energy Survey Year-1 Galaxy Clusters
• Covariance in the Thermal SZ-Weak Lensing Mass Scaling Relation of Galaxy Clusters
• Projection effects in galaxy cluster samples: insights from X-ray redshifts
• The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe

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• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• The Three Hundred Project: quest of clusters of galaxies morphology and dynamical state through Zernike Polynomials
• Dynamical mass inference of galaxy clusters with neural flows
• Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
• Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
• Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
• What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest
• Aging Halos: Implications of the Magnitude Gap on Conditional Statistics of Stellar and Gas Properties of Massive Halos
• Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses
• Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders
• The Importance of Being Interpretable: Toward An Understandable Machine Learning Encoder for Galaxy Cluster Cosmology
• Cosmology with Galaxy Cluster Properties using Machine Learning
• Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
• Constraining Galaxy-Halo Connection Using Machine Learning
• AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters