ChronoFlow: A Data-Driven Model for Gyrochronology

Music City Center 

Stellar ages are critical to astronomy on a wide range of scales, but challenging to measure for low mass main sequence stars. One method that is promising for such stars is gyrochronology, which uses the evolution of their rotation rates, or "spindown". However, analytical gyrochronology models have historically struggled to capture the observed rotational dispersion in stellar populations. To properly understand this complexity, we have developed ChronoFlow: a flexible data-driven model built using a conditional normalizing flow. We show that it accurately captures observed rotational dispersion in open clusters, and we also apply ChronoFlow within a Bayesian inference framework to infer stellar ages. We recover cluster ages with a statistical uncertainty of 0.06 dex (≈ 15%), and individual stellar ages with a statistical uncertainty of 0.7 dex. Additionally, we conducted robust systematic tests to analyze the impact of extinction models, cluster membership, and calibration ages on ChronoFlow's performance. Our results show that ChronoFlow can estimate the ages of coeval stellar populations to the precision of the best literature models, and that it performs better for clusters of ages ~50-200 Myr than existing data-driven models. ChronoFlow is publicly available at https://github.com/philvanlane/chronoflow.