A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology

• URL: http://arxiv.org/abs/2307.08753v1
• Date: Mon, 17 Jul 2023 18:00:19 GMT
• Title: A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology
• Authors: Phil Van-Lane (1), Joshua S. Speagle (2 and 1 and 3 and 4), Stephanie Douglas (5) ((1) Department of Astronomy & Astrophysics, University of Toronto, Canada, (2) Department of Statistical Sciences, University of Toronto, Canada, (3) Dunlap Institute of Astronomy & Astrophysics, University of Toronto, Canada, (4) Data Sciences Institute, University of Toronto, Canada, (5) Department of Physics, Lafayette College, United States)
• Abstract summary: We show that a data-driven approach can constrain gyrochronological ages with a precision comparable to other standard techniques. This work demonstrates the potential of a probabilistic data-driven solution to widen the applicability of gyrochronological stellar dating.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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