A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology
This work provides a probabilistic data-driven solution to improve gyrochronological stellar dating, which is incremental as it applies an existing machine learning method to a new domain-specific problem in astronomy.
The paper tackled the challenge of measuring stellar ages for low mass main sequence stars by applying conditional normalizing flows to photometric data from open star clusters, achieving inferred ages that recover literature values with precision comparable to other standard techniques.
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.