1 citations · 1 across the 2 of their papers we have counts for
5 papers
A simulation-based inference of the Milky Way merger history
Andrea Sante, Andreea S. Font, Daisuke Kawata +2
Accreted stars in the Milky Way (MW) preserve information about the progenitor galaxies where they formed in their chemical and kinematic properties. In this study, we use the chem…
Optimized HDBSCAN clustering for reconstructing the merger history of the Milky Way: applications and limitations
Andrea Sante, Andreea S. Font, Dharmesh Mistry +2
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 sp…
Measuring and modelling the Splash with APOGEE/Gaia and ARTEMIS
Shobhit Kisku, Ricardo P. Schiavon, Andreea S. Font +6
Using combined data from SDSS-IV/APOGEE and Gaia, we study the chemo-dynamical properties of the Splash population in comparison with those of the high-alpha disc. We investigate a…
GalactiKit: reconstructing mergers from debris using simulation-based inference in Auriga
Andrea Sante, Daisuke Kawata, Andreea S. Font +1
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 gala…
Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
Andrea Sante, Andreea S. Font, Sandra Ortega-Martorell +2
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later a…