RT info:eu-repo/semantics/bachelorThesis
T1 Searching for compaction in the TNG50 cosmological simulation using deep learning
A1 Iglesias Navarro, Patricia
K1 galaxies: evolution
K1 galaxies: high-redshift
K1 galaxies: quenching
K1 cosmology
K1 galaxies: evolution
K1 galaxies: fundamental parameters
K1 galaxies: high-redshift
K1 galaxies: quenching
AB We optimize a convolutional neural network, intending to study an astrophysical processknown as ‘blue nuggets’ (BN), which consists of a compaction followed by a central quenchingthat occurs in young galaxies at high redshifts. This network is evaluated with mock ‘observed’images of galaxies at three phases of evolution (Pre-BN, BN and Post-BN), generated by thezoom-in hydro-cosmological simulation VELA. We then use this to classify galaxies from theTNG50 simulation in these three phases, and finally, we study their physical properties suchas the redshift, the effective radius and the star formation rate (SFR), as well as the massesof gas, of stars, and of the central supermassive black holes. The network successfully detectsthis compaction phase in the new simulation, consistent with the features observed in VELAgalaxies. We highlight the existence of a temporal sequence, together with the fact that theBN phase forms stars while the Post-BN does not. Furthermore, the BN phase is associatedwith a gas mass peak at z ∼ 2 and with a smaller radius.
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/25018
UL http://riull.ull.es/xmlui/handle/915/25018
LA es
DS Repositorio institucional de la Universidad de La Laguna
RD 13-may-2024