Abstract: Lambda-CDM cosmology has been thoroughly validated on large scales, with the fundamental tenets of cold dark matter remaining robust despite a few potential tensions. Dark matter must be cold at those scales. On small scales, however, a variety of persisting and new discrepancies exist between theoretical predictions and observations. This is particularly evident in the Milky Way, which exhibits a significantly uncertain distribution of satellite galaxies in terms of both mass and radial distance. Understanding dark matter substructures becomes crucial in light of these uncertainties. While numerical simulations are key for quantifying these substructures, the suitability of standard tools, such as halo finders and merger trees, for identifying and tracking subhalos in simulations is increasingly questioned, especially for low-mass subhalos orbiting deep within the potential of the host halo.
To address this challenge, I have developed and rigorously tested a subhalo tracking package named Bloodhound, optimized for accurately tracking subhalos even in the densest regions of the host halo. In this talk, I will discuss the current status of some key small-scale challenges of Lambda-CDM and highlight Bloodhound's contributions to refining the statistical properties of dark matter subhalos in Phat ELVIS cosmological simulations. These refined predictions will be directly compared with observations of the Milky Way’s dwarf satellite galaxies from the JWST and the forthcoming Vera Rubin Observatory, offering rigorous tests of the Lambda-CDM cosmological paradigm as well as the galaxy formation theory. Alignment between predicted subhalo counts and observed satellite galaxies would provide a powerful confirmation for Lambda-CDM. Conversely, any discrepancies may prompt a reassessment and exploration of alternative models, such as self-interacting dark matter.