paper

Determining the Dark Matter distribution in galaxies with Deep Learning

arXiv:2111.08725 · doi:10.1093/mnras/stad2614

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 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

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