Oral Presentation
Fitting Simulated Galaxies to Observations
Presenter: Suchetha Cooray (Nagoya University)
We propose a data-driven model of galaxy star formation histories (SFHs). Extracting a galaxy’s SFH from its observed spectral energy distribution is challenging. Therefore, SFHs are commonly approximated with parametric and nonparametric models that either impose strong priors on the measurements or are poorly constrained. Physically-motivated SFH models from simulations may then be favored. Instead of building an extensive library of simulated SFHs, we propose using a recent machine learning (ML) technique called generative modeling. These ML models learn the intrinsic distribution of SFHs from simulations and can generate new physically-motivated SFHs, which can be fitted to observations. We demonstrate the performance of fitting generated SFHs using IllustrisTNG and UniverseMachine simulations. The advantages of such methods are bidirectional, to constrain the galaxy property measurements from observed SEDs, and to constrain galaxy formation models from observations.
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