Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning

Abstract

Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80\% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars, Sunyaev-Zeldovich, and X-ray, by training deep learning models. To this end, we have created a multiview images dataset from {\sc{The Three Hundred}} simulation that is optimal for training Machine Learning models. We further study deep learning architectures based on the U-Net to account for single-input and multi-input models. We show that the predicted mass distribution agrees well with the true one.

To appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

References in corpus (24)

• Planck 2015 results. XIII. Cosmological parameters
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• MultiDark simulations: the story of dark matter halo concentrations and density profiles
• Ahf: Amiga's Halo Finder
• Formation of Galaxy Clusters
• The Three Hundred project: a large catalogue of theoretically modelled galaxy clusters for cosmological and astrophysical applications
• The SPTpol Extended Cluster Survey
• The eROSITA Final Equatorial-Depth Survey (eFEDS): Catalog of galaxy clusters and groups
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• A Deep Learning Approach to Galaxy Cluster X-ray Masses
• The Three Hundred Project: Dynamical state of galaxy clusters and morphology from multi-wavelength synthetic maps
• Exploring the hydrostatic mass bias in MUSIC clusters: application to the NIKA2 mock sample
• A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
• CCCP and MENeaCS: (updated) weak-lensing masses for 100 galaxy clusters
• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• Dynamical mass inference of galaxy clusters with neural flows
• A study of the hydrostatic mass bias dependence and evolution within The Three Hundred clusters
• A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton$-y$ parameter maps
• Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
• Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
• Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
• Mass Estimation of Galaxy Clusters with Deep Learning II: CMB Cluster Lensing
• The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps
• Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses