| Full text | |
| Author(s): |
de los Rios, Martin
;
Petac, Mihael
;
Zaldivar, Bryan
;
Bonaventura, Nina R.
;
Calore, Francesca
;
Iocco, Fabio
Total Authors: 6
|
| Document type: | Journal article |
| Source: | Monthly Notices of the Royal Astronomical Society; v. 525, n. 4, p. 21-pg., 2023-09-11. |
| 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. (AU) | |
| FAPESP's process: | 19/08852-2 - Machine learning techniques applied to cosmological problems |
| Grantee: | Martín Emilio de los Rios |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |