Kepler stars predicted rotation period: Deep Learning

Our evaluation demonstrates that LightPred outperforms classical methods like the autocorrelation function in terms of accuracy and average error. We apply LightPred to the Kepler data set, generating the largest catalog to date. Using error analysis based on learned confidence and consistency metric, we were able to filter the predictions and remove stellar types with variability, which is different than spot-induced variability.

Our analysis shows strong correlations between error levels and stellar parameters. Additionally, we confirm tidal synchronization in eclipsing binaries with orbital periods shorter than 10 days. Our findings highlight the potential of deep learning in extracting fundamental stellar properties from light curves, opening new avenues for understanding stellar evolution and population demographics.