Speaker
Description
Machine learning techniques are widely implemented to predict galaxy properties based on halo or subhalo properties in semi-analytic models (SAM) and hydrodynamic simulations. The multivariate relation between galaxies and their (sub)halos can be efficiently captured and reproduced. I will present our results on predicting galaxy properties based on different galaxy formation models. Additionally, I will also discuss the impact of baryonic processes on galaxy properties. These studies can provide helpful insights into galaxy formation and evolution. However, the galaxy-(sub)halo relation in galaxy formation models may deviate from those in the real Universe. With the ELUCID simulation which is constructed to reproduce the density field of the SDSS and the SDSS-ELUCID matched catalog which links SDSS galaxies to ELUCID subhalos, we repeat the ML to predict SDSS galaxy properties based on ELUCID subhalo properties. I will show the differences in ML results compared to those based on the galaxy formation models. This comparison is helpful for understanding the galaxy-halo connection in observation.
