Galaxy morphologies in cosmological simulations: The sizes and morphologies of galaxies hosting long gamma-ray bursts

BIGNONE, L.A.; TISSERA, P.B.; PELLIZZA, L.J.; PEDROSA, S.E.

La Serena

Congreso; Segunda Reunión Binacional de la Asociación Argentina de Astronomía y la Sociedad Chilena de Astronomía; 2018

Context: Recent developments in cosmological simulations of galaxy formation have resulted in an increasedagreement between the sizes of simulated galaxies and observations. Furthermore, it is now possible to generatemock galaxy images where the comparison between the light distribution of simulated and observed galaxies canbe done at an unprecedented granular detail. The galaxies hosting Long Gamma-Ray Bursts (LGRBs) have beenfound to possess remarkable characteristics in their sizes and morphologies that could be linked to properties ofthe LGRB progenitors. This new developments in numerical simulations can, therefore, be used to understandthe morphology of LGRBs host galaxies and their nature.Aims: Our goal is to study the sizes and morphologies of galaxies hosting LGRBs and to determine if theirproperties are indicative of a single or a binary progenitor star scenario. Furthermore, these galaxies have beenclaimed to be smaller than other star-forming galaxies of the same mass which could be an indication of a top-heavy initial mass function in overdense regions. Also, mock images of galaxy population allow us to determinenon-parametric morphologies and to, for the first time, directly compare the morphologies of observed hosts andthose predicted by simulations.Methods: We construct LGRBs and host galaxy model populations under different assumptions on the nature ofthe LGRB progenitors. To do so we utilize simulated galaxy catalogs built from the EAGLE simulation, a largescale cosmological hydrodynamical simulations. We explore the ability of these models to reproduce observed hostsizes. We further determine the predicted morphologies by generating mock galaxy observations of the simulatedhosts and computing non-parametric morphologies. Our methods can easily be used to study the hosts of otherstellar populations or galaxies in general. Our numerical approach and the systematic morphological determinationwe employ could be further enhance using machine learning approaches for the automatic classification of galaxiesof interest, which will be of great importance in the near future with the advent of truly large-scale galaxy surveyssuch as LSST.