Interpreting Stellar Spectra with Unsupervised Domain Adaptation
  We discuss how to achieve mapping from large sets of imperfect simulations
and observational data with unsupervised domain adaptation. Under the
hypothesis that simulated and observed data distributions share a common
underlying representation, we show how it is possible to transfer between
simulated and observed domains. Driven by an application to interpret stellar
spectroscopic sky surveys, we construct the domain transfer pipeline from two
adversarial autoencoders on each domains with a disentangling latent space, and
a cycle-consistency constraint. We then construct a differentiable pipeline
from physical stellar parameters to realistic observed spectra, aided by a
supplementary generative surrogate physics emulator network. We further
exemplify the potential of the method on the reconstructed spectra quality and
to discover new spectral features associated to elemental abundances.
