Unsupervised Domain Adaptation for Constraining Star Formation Histories
  The prevalent paradigm of machine learning today is to use past observations
to predict future ones. What if, however, we are interested in knowing the past
given the present? This situation is indeed one that astronomers must contend
with often. To understand the formation of our universe, we must derive the
time evolution of the visible mass content of galaxies. However, to observe a
complete star life, one would need to wait for one billion years! To overcome
this difficulty, astrophysicists leverage supercomputers and evolve simulated
models of galaxies till the current age of the universe, thus establishing a
mapping between observed radiation and star formation histories (SFHs). Such
ground-truth SFHs are lacking for actual galaxy observations, where they are
usually inferred -- with often poor confidence -- from spectral energy
distributions (SEDs) using Bayesian fitting methods. In this investigation, we
discuss the ability of unsupervised domain adaptation to derive accurate SFHs
for galaxies with simulated data as a necessary first step in developing a
technique that can ultimately be applied to observational data.
