Improving the Physical Interpretability of Gaussian Processes in Stellar Activity Modeling: A Study Case on Photometric Variability Among Stellar Clusters
Gaussian Processes (GPs) are widely used to model stellar variability in photometric surveys, but a statistically successful fit does not guarantee that the inferred hyperparameters correspond to physically meaningful stellar properties. This is especially important for young, active stars, whose TESS light curves contain evolving spots, harmonics, and non-sinusoidal variability. We use stellar rotation as a case study to examine when the period hyperparameter of a quasi-periodic GP can be interpreted as a physical rotation period. We introduce a regularized GP likelihood that reweights the covariance-complexity term in the marginal likelihood, reducing the tendency of unconstrained models to converge toward preferred but misleading solutions. We test this framework on 539 stars in IC2602, the Tucana-Horologium Association, Pisces-Eridanus, and GroupX, with independently reported and manually vetted rotation periods. This benchmark evaluates GP hyperparameter interpretability and automated period recovery with minimal human intervention. We compare regularized and unregularized models across several regularization strengths, {\lambda}, using literature agreement, sector-level diagnostics, and consistency across TESS sectors. Relative to the standard GP likelihood, regularization improves successful rotation-period recovery by an average of 7%. It also generally reduces the median absolute fractional deviation of periods across sectors, showing that the improvement is not limited to catastrophic failures but also mitigates smaller systematic errors. Regularization is particularly beneficial for non-sinusoidal or evolving modulation, where unconstrained GPs may recover harmonics or spurious timescales. These results show that likelihood regularization and cross-sector consistency are practical diagnostics for assessing when GP-based rotation periods are robust.