FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records

TL;DR

This paper introduces FLARE, a novel large model that combines stellar physical properties and historical flare data to improve stellar flare forecasting, demonstrated on Kepler data with superior results.

Contribution

We propose FLARE, the first specialized model for stellar flare forecasting that integrates physical and historical data using innovative modules.

Findings

FLARE outperforms existing methods across all metrics.

Combining physical properties and historical records enhances forecast accuracy.

The model's effectiveness is validated through a comprehensive case study.

Abstract

Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Our experiments on the publicly available Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.