Assembly and fate of the population of ultra-faints in the Milky Way.

Abstract

The Gaia data release revealed that the stellar halo is dominated by a highly anisotropic and non-Gaussian structure pointing to the remains of a massive merger at high-redshift now better known as the Gaia-Sausage-Enceladus (GSE) merger event (Belokurov et al. 2018, Helmi et al. 2018). While much work has focused on the properties of the Sausage (e.g. Fattahi et al. 2019), little is known about its satellite population (Bose et al. 2020). The aim of this project is to shed some light on the population of galaxies that came with the Sausage galaxy through group infall.

In this project we aim to characterize the accretion history of the Milky Way using a statistical sample of MW-like realisations in order to place the Galaxy in a cosmological context. We will look for systems with accretion histories resembling that of the Galaxy (e.g. those with Gaia-Sausage-Enceladus-like satellites merging at high-z vs those without) and characterise their population of satellite galaxies in particular at the low-mass end (the so-called ultra-faint galaxies, UFDs). We will use the Munich semi-analytic model (Henriques et al. 2020) to track the evolution of ultra-faints and identify systems GSE-host MW-like systems and characterise their luminosity function and chemical properties of their UFDs and compare them to rest of the population of MW-like halos. We will then also look into Auriga (Grand et al. 2017) and TNG50 to find analogues of the Sausage and study their satellite they brought with them as well as their survival and compare them with observations of the stellar halo with Gaia and legacy spectroscopic surveys. Interesting systems will be used to set-up some dynamical numerical experiments to study the fate, distribution and properties of the remains of the galaxies brought with the GSE merger.

Advisors
Chervin Laporte, João Amarante, Matthew Orkney
Requirements
basic programming in Python or IDL and some knowledge of galaxy formation, but can also be learned during the project.
References

1) Belokurov et al., 2018, MNRAS, 478, 611: https://ui.adsabs.harvard.edu/abs/2018MNRAS.478..611B/abstract
2) Helmi et al., 2018, Nature, 563, 85: https://ui.adsabs.harvard.edu/abs/2018Natur.563...85H/abstract
3) Fattahi et al. 2019, MNRAS, 484, 4471: https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4471F/abstract
4) Bose et al. 2020, MNRAS, 495, 743: https://ui.adsabs.harvard.edu/abs/2020MNRAS.495..743B/abstract
5) Henriques et al. 2020, MNRAS, 491, 5795 : https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5795H/abstract
6) Grand et al. 2017, MNRAS, 467, 179: https://ui.adsabs.harvard.edu/abs/2017MNRAS.467..179G/abstract
7) Pillepich et al. 2019, MNRAS, 490, 3196: https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3196P/abstract