Inferring Halo Mass and Scale Radius of Galaxy Clusters Using Convolutional Neural Networks and Uchuu-UniverseMachine Catalogs

TL;DR

This study uses convolutional neural networks trained on simulated galaxy cluster data to accurately predict their virial mass and scale radius from observational features, enhancing cosmological analysis.

Contribution

It introduces a CNN-based approach that infers cluster properties from mock observations, with improved accuracy when using additional data like richness and relaxed cluster selection.

Findings

CNNs achieve nearly unbiased residuals within 0.01 dex for mass and radius.

Adding richness reduces residual scatter for mass estimates.

Focusing on relaxed clusters improves radius prediction accuracy.

Abstract

We investigate the ability of machine learning to infer the virial mass ($M_{\rm vir}$) and the scale radius ($r_{\rm s}$) of galaxy clusters from their observables. Using the Uchuu--UniverseMachine galaxy catalog at $z=0.093$, we generate mock cluster observations that include interlopers, and we encode each cluster as an image representing the two-dimensional joint probability distribution of member galaxies' projected position and line-of-sight velocity. We train two architectures: a baseline convolutional neural network (CNNb) following a previous approach, and an extended model (CNNr) that appends richness as an additional scalar input. We further compare the performance of networks trained on the all cluster sample and on a dynamically relaxed subsample. Across the test ranges $10^{13.7}\leq M_{\rm vir}\leq10^{15.3}$ Msun/h and $10^{1.7}\leq r_{\rm s}\leq10^{2.7}$ kpc/h, all configurations yield nearly unbiased absolute median residuals (within 0.01 dex). For the halo mass, adding richness narrows the residual distribution, reducing the standard deviation from 0.133 to 0.122 dex for the all sample, and from 0.124 to 0.111 dex for the relaxed sample. For the scale radius, restricting the training to relaxed clusters improves the performance more than adding richness. The standard deviation decreases from 0.180 to 0.154 dex for CNNb and from 0.175 to 0.148 dex for CNNr, while the inclusion of richness yields only a modest improvement of 0.005 dex. These results demonstrate that machine learning is a powerful tool to infer the mass and internal mass distribution of clusters, providing a new window for cosmological inferences and understanding galaxy formation processes.