Determining the Dark Matter distribution in galaxies with Deep Learning

TL;DR

This paper introduces a deep learning approach using convolutional neural networks trained on hydrodynamical simulations to accurately infer the dark matter distribution in galaxies across various mass ranges, surpassing traditional methods.

Contribution

The novel framework leverages CNNs trained on Illustris TNG100 simulations to estimate dark matter profiles without assuming specific shapes or requiring spectroscopic data.

Findings

High accuracy in DM distribution inference across galaxy types

Applicable to galaxies in different environments without shape assumptions

Effective even without spectroscopic observations

Abstract

We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hydrodynamical simulations (Illustris TNG100). The framework we have developed is capable of inferring the DM mass distribution within galaxies of mass $~10^{11}-10^{13}M_{\odot}$ with very high performance from the gravitationally baryon dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations