Andrea Sante

Accreted stars in the Milky Way (MW) preserve information about their galaxies of origin in their chemo-dynamical properties. In this study, we use the chemo-dynamical signatures in the merger debris to approximate the posterior distribution of the properties of disrupted satellites at their accretion. Adopting a simulation-based inference framework, we train an ensemble of normalizing flows using samples of merger debris from the Auriga simulations. Applying this methodology to a local sample of accreted stars in the MW, we infer the lookback times, stellar and halo masses, and halo mass merger ratios of several known accretion events: Gaia-Enceladus-Sausage (GES), Helmi streams, Heracles, I’itoi, LMS-1/Wukong, Sagittarius, Sequoia, and Thamnos. Our predictions for accretion time and mass align with previous estimates and follow the expected stellar mass–metallicity relation across redshifts. The total accreted stellar mass from these events is predicted to be $2.2^{+1.1}_{-0.6}\times 10^{9}~\rm {M_{\odot }}$, with the GES and Sagittarius dwarfs being the largest contributors. The total accreted stellar mass from all disrupted progenitors is $1.4^{+1.1}_{-0.4}\times 10^{9}~\rm {M_{\odot }}$, which is consistent with previous mass measurements of this component. We provide a prediction for the evolution of the MW halo mass until the accretion of Sagittarius ($z\approx 1$): We find that the halo mass growth from the time of its first merger ($z\approx 5$) to $z\approx 2$ significantly exceeds the total mass of the known progenitors accreted during that interval, suggesting the presence of unidentified substructures. Our estimate of the Galaxy halo mass after the Sagittarius merger, but prior to the accretion of the Magellanic Clouds, is $5.9^{+1.4}_{-1.1}\times 10^{11}~\rm {M_{\odot }}$.

Clustering algorithms can help reconstruct the assembly history of the Milky Way by identifying groups of stars sharing similar properties in a kinematical or chemical abundance space. Despite being promising tools, their efficiency has not yet been fully tested in a realistic cosmological framework. We investigate the effectiveness of the hdbscan clustering algorithm in the recovery of the progenitors of Milky Way-type galaxies, using several systems from the Auriga suite of simulations. We develop a methodology aimed at improving the efficiency of the algorithm and avoiding fragmentation: first, we use a 12-dimensional feature space including a range of chemodynamical properties and stellar ages; furthermore, we optimize the algorithm using information from the internal structure of the clusters of accreted stars. We show that our approach yields good results in terms of both purity and completeness of clusters for galaxies with different types of accretion histories. We also evaluate the decrease in efficiency due to contamination by in situ stars. While for accreted-only haloes the algorithm matches well the recovered clusters with the individual progenitors and is able to recover accretion events up to a redshift of accretion $z_{\rm acc}\sim 3$, for accreted + in situ haloes it can only identify the more recent accretion events ($z_{\rm acc} \lt 1$). However, the purity of the identified clusters remains remarkably high even in this case. Our results suggest that hdbscan can efficiently identify accreted debris in Milky Way-type galaxies in realistic conditions, however, it requires careful optimization to provide valid results.

We present GalactiKit, a data-driven methodology for estimating the lookback infall time, stellar mass, halo mass, and mass ratio of the disrupted progenitors of Milky Way-like galaxies at the time of infall. GalactiKit uses simulation-based inference to extract the information on galaxy formation processes encoded in the Auriga cosmological magnetohydrodynamic (MHD) simulations of Milky Way-mass haloes to create a model that relates the properties of mergers to those of the corresponding merger debris at $z=0$. We investigate how well GalactiKit can reconstruct the merger properties given the dynamical, chemical, and the combined chemodynamical information of debris. For this purpose, three models were implemented considering the following properties of merger debris: (a) total energy and angular momentum, (b) iron-to-hydrogen and alpha-to-iron abundance ratios, and (c) a combination of all of these. We find that the kinematics of the debris can be used to trace the lookback time at which the progenitor was first accreted into the main halo. However, chemical information is necessary for inferring the stellar and halo masses of the progenitors. In both models (b) and (c), the stellar masses are predicted more accurately than the halo masses, which could be related to the scatter in the stellar mass–halo mass relation. Model (c) provides the most accurate predictions for the merger parameters, which suggests that combining chemical and dynamical data of debris can significantly improve the reconstruction of the Milky Way’s assembly history.