Convolutional Neural Networks for Searching Superflares from Pixel-level Data of the Transiting Exoplanet Survey Satellite

TL;DR

This study develops and evaluates convolutional neural networks trained on pixel-level TESS data to automatically identify superflares on solar-type stars, achieving high accuracy and reducing false positives compared to traditional visual inspection methods.

Contribution

The paper introduces a CNN-based approach using pixel-level TESS data for superflare detection, outperforming previous light curve-only methods and providing publicly available trained models and datasets.

Findings

Ensemble learning improves accuracy to 97.62% and 100% classification rate.

Voting method achieves 99.42% accuracy with 92.19% classification rate.

Short-duration, low-amplitude superflares are harder to identify.

Abstract

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years observations of Transiting Exoplanet Survey Satellite ({\em TESS}). These networks are used to replace the artificially visual inspection, which was a direct way to search for superflares, and exclude false positive events in recent years. Unlike other methods, which only used stellar light curves to search superflare signals, we try to identify superflares through {\em TESS} pixel-level data with lower risks of mixing false positive events, and give more reliable identification results for statistical analysis. The evaluated accuracy of each network is around 95.57\%. After applying ensemble learning to these networks, stacking method promotes accuracy to 97.62\% with 100\% classification rate, and voting method promotes accuracy to 99.42\% with relatively lower classification rate at 92.19\%. We find that superflare candidates with short duration and low peak amplitude have lower identification precision, as their superflare-features are hard to be identified. The database including 71,732 solar-type stars and 15,638 superflare candidates from {\em TESS} with corresponding feature images and arrays, and trained CNNs in this work are public available.