Stellar Age Inference with Rotation (+ Activity?)

Ages of low mass main sequence (MS) stars can be challenging to measure because of their long MS lifetimes. While gyrochronology (a method of dating such stars using rotation period) is somewhat effective, the observed dispersion in rotation rates for similar coeval stars has historically been difficult to model. To characterize this complexity, we present the largest standardized catalogue of rotators in open clusters to date, which we have used to develop ChronoFlow: a state-of-the-art machine learning framework that can be used to forward model rotational evolution and to infer stellar ages. Additionally, we present the results of robust systematic tests in which we quantify the impact of extinction models, cluster membership, and calibration techniques on age estimates. Building on this, we explore whether other manifestations of magnetic activity in Kepler/K2/TESS light curves (such as photometric variability and flaring) can provide age information that is complementary to rotation, and we test whether joint activity-rotation models can constrain stellar ages better than rotation alone.