Accurate and Robust Stellar Rotation Periods catalog for 82771 Kepler stars using deep learning

Abstract: We propose a new framework to predict stellar properties from light curves. We analyze the light-curve data from the Kepler space mission and develop a novel tool for deriving the stellar rotation periods for main-sequence stars. Using this tool, we provide rotation periods for more than 80K stars. Our model, LightPred, is a novel deep-learning model designed to extract stellar rotation periods from light curves. The model utilizes a dual-branch architecture combining Long Short-Term Memory (LSTM) and Transformer components to capture temporal and global data features. We train LightPred on self-supervised contrastive pre-training and simulated light curves generated using a realistic spot model. Our evaluation demonstrates that LightPred outperforms classical methods like the Autocorrelation Function (ACF) in terms of accuracy and average error. We apply LightPred to the Kepler dataset, 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.