FCN4Flare: Fully Convolution Neural Networks for Flare Detection

TL;DR

FCN4Flare employs fully convolutional neural networks with innovative masking techniques to accurately detect stellar flares in large photometric datasets, surpassing previous methods and enabling new astrophysical insights.

Contribution

This work introduces FCN4Flare, a novel deep learning model with NaN Mask and Mask Dice loss for precise flare detection in variable-length light curves, improving over prior approaches.

Findings

Achieved a Dice coefficient of 0.64, outperforming the previous 0.12.

Compiled a catalog of 30,285 high-confidence flares from Kepler data.

Identified stellar activity patterns, including active M-dwarfs with habitable zone planets.

Abstract

Stellar flares offer invaluable insights into stellar magnetic activity and exoplanetary environments. Automated flare detection enables exploiting vast photometric datasets from missions like Kepler. This paper presents FCN4Flare, a deep learning approach using fully convolutional networks (FCN) for precise point-to-point flare prediction regardless of light curve length. Key innovations include the NaN Mask to handle missing data automatedly, and the Mask Dice loss to mitigate severe class imbalance. Experimental results show that FCN4Flare significantly outperforms previous methods, achieving a Dice coefficient of 0.64 compared to the state-of-the-art of 0.12. Applying FCN4Flare to Kepler-LAMOST data, we compile a catalog of 30,285 high-confidence flares across 1426 stars. Flare energies are estimated and stellar/exoplanet properties analyzed, identifying pronounced activity for an M-dwarf hosting a habitable zone planet. This work overcomes limitations of prior flare detection methods via deep learning, enabling new scientific discoveries through analysis of photometric time-series data. Code is available at https://github.com/NAOC-LAMOST/fcn4flare .