TY  - JOUR
AU  - de los Rios, M.
AU  - Petac, M.
AU  - Zaldivar, B.
AU  - Bonaventura, N. R.
AU  - Calore, F.
AU  - Iocco, F.
PY  - 2023
DA  - 2023//
TI  - Determining the dark matter distribution in simulated galaxies with deep learning
T2  - Mon. Not. Roy. Astron. Soc.
JO  - Monthly Notices of the Royal Astronomical Society
SP  - 6015
EP  - 6035
VL  - 525
IS  - 4
PB  - Oxford Univ Press
KW  - methods: data analysis
KW  - software: simulations
KW  - galaxies: general
KW  - galaxies: haloes
KW  - dark matter
AB  - We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. 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.
SN  - 0035-8711
UR  - https://arxiv.org/abs/2111.08725
UR  - https://doi.org/10.1093/mnras/stad2614
DO  - 10.1093/mnras/stad2614
LA  - English
N1  - WOS:001072112100006
ID  - delosRios_etal2023
ER  -