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.

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

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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