A Novel Application of Conditional Normalizing Flows: Stellar Age
  Inference with Gyrochronology
Phil Van-Lane Joshua S. Speagle Stephanie Douglas
Stellar ages are critical building blocks of evolutionary models, but
challenging to measure for low mass main sequence stars. An unexplored solution
in this regime is the application of probabilistic machine learning methods to
gyrochronology, a stellar dating technique that is uniquely well suited for
these stars. While accurate analytical gyrochronological models have proven
challenging to develop, here we apply conditional normalizing flows to
photometric data from open star clusters, and demonstrate that a data-driven
approach can constrain gyrochronological ages with a precision comparable to
other standard techniques. We evaluate the flow results in the context of a
Bayesian framework, and show that our inferred ages recover literature values
well. This work demonstrates the potential of a probabilistic data-driven
solution to widen the applicability of gyrochronological stellar dating.