The Maunder Model and Catalog: Stellar Rotation, Bimodal Activity, and Magnetic Braking in Kepler Main-Sequence Stars

Abstract

We present The Maunder, a machine learning pipeline and resulting catalog of rotation periods for 148,746 main-sequence stars in the Kepler field. To overcome single-catalog systematics and the simulation-to-reality gap, our architecture employs a hybrid training objective: a joint-embedding self-supervised loss applied to all light curves, combined with a supervised loss trained strictly on cross-catalog consensus labels. By processing multi-scale time- and frequency-domain inputs over rolling windows, the model leverages conformalized quantile regression to output calibrated predictive intervals, providing statistically robust per-star rotation uncertainty metrics. This rolling-window inference reveals that 31,953 stars (21.5$\%$) exhibit bimodal rotational signals. By incorporating APOGEE $v \sin i$ measurements, we demonstrate that for distinct (non-harmonic) bimodals, the longer mode represents the true rotation, exposing a systematic failure mode wherein classical single-pass periodograms lock onto shorter aliases. Filtering by our calibrated confidence intervals yields a highly reliable subset of 119,428 stars. The catalog resolves various rotation-related phenomena: the metallicity dependence of rotation at fixed stellar mass, pointing on the role of metallicity in magnetic braking processes; tracing equatorial velocity and specific angular momentum directly across the Kraft break; recovery of empirical gyrochronology sequences and identification of hierarchical triple candidates among the synchronized-binary population. \emph{The Maunder} provides reliable rotation periods for the largest main-sequence population in \textit{Kepler}, allowing for population-level studies of rotation-based phenomena.