Title: Decoding Galaxy SEDs: Advances and Challenges in Recovering Star Formation Histories
Abstract:
The spectral energy distribution (SED) of galaxies encodes valuable information on a number of astrophysical processes in galaxy evolution. Currently, the standard method for modeling galaxy SEDs is stellar population synthesis, which constructs SED using many components, including the initial mass function, star formation history (SFH), and metal enrichment history, among others. By decoding galaxy SEDs through forward-modeling the synthesized SED and adjusting model parameters, many physical properties can be extracted. Among these properties, deriving an accurate SFH is one of the major challenges in SED fitting, given both its pivotal role in galaxy evolution and the inherent modeling complexities. Current SFH recovery methods generally adopt three distinct paradigms: parametric models employing analytic functional forms, nonparametric approaches utilizing broad step functions with high flexibility, and emerging methods that incorporate SFH libraries derived from semi-analytic models or hydrodynamic simulations. In this talk, I will introduce the prevailing SFH priors, analyzing their respective strengths and weaknesses.
