Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks

TL;DR

This paper introduces a novel simulation-based inference framework using 3D convolutional neural networks to estimate galaxy cluster masses from their phase-space distribution, effectively handling uncertainties and interlopers.

Contribution

It is the first to apply machine learning with simulation-based inference to utilize full 3D phase-space data for dynamical mass estimation of galaxy clusters.

Findings

Achieved consistent mass estimates for ~800 SDSS galaxy clusters.

Demonstrated robustness of the method against interloper contamination.

Quantified uncertainties in mass estimates effectively.

Abstract

We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts $z \lesssim 0.09$ and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around $800$ galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature.