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Author (up) de los Rios, M.; Petac, M.; Zaldivar, B.; Bonaventura, N.R.; Calore, F.; Iocco, F.
Title Determining the dark matter distribution in simulated galaxies with deep learning Type Journal Article
Year 2023 Publication Monthly Notices of the Royal Astronomical Society Abbreviated Journal Mon. Not. Roy. Astron. Soc.
Volume 525 Issue 4 Pages 6015-6035
Keywords methods: data analysis; software: simulations; galaxies: general; galaxies: haloes; dark matter
Abstract 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.
Address [de los Rios, Martin] Univ Estadual Paulista, ICTP South Amer Inst Fundamental Res, Inst Fis Teor, BR-01140070 Sao Paulo, SP, Brazil, Email: fabio.iocco.astro@gmail.com
Corporate Author Thesis
Publisher Oxford Univ Press Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0035-8711 ISBN Medium
Area Expedition Conference
Notes WOS:001072112100006 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5707
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