Speaker
Description
Subhalo abundance matching (SHAM) is a widely used empirical model for linking galaxies to (sub)halos and predicting galaxy clustering. It assigns galaxies to subhalos by matching the cumulative number density of galaxies above a luminosity (or stellar mass) threshold to the corresponding number density of subhalos above a mass threshold. Scatters in the stellar-to-halo mass relation can also be incorporated into the model. Compared to the halo occupation distribution (HOD) model, SHAM offers distinct advantages: it requires far fewer free parameters and naturally reproduces galaxy distributions across a broad luminosity range.. However, the simplest SHAM struggles to fit galaxy clustering in small scales accurately. To address this limitation, previous studies have explored improvements such as using peak maximum circular velocity (Vpeak) instead of subhalo mass and introducing orphan galaxies to account for tidally disrupted subhalos. We propose a new SHAM model including new parameters to describe the satellite fraction as function of stellar mass. This modification enables tuning of galaxy clustering on small scales. We test this SHAM using mock galaxies, SAM, and hydrodynamic simulation, demonstrating that it simultaneously achieves reasonable fits to galaxy clustering and recovers the underlying true satellite fraction. This new SHAM model provides a powerful tool for interpreting observed galaxy clustering and generating realistic mock galaxy catalogs from large-volume N-body simulations.
