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

2025-02-25

AI Summary

Addressing the scarcity of stellar flare samples and the absence of dedicated forecasting models, this paper introduces the first large-scale physics-enhanced model for flare time-series prediction. Methodologically, it innovatively designs a Soft Prompt Module and a Residual Record Fusion Module, enabling, for the first time, joint modeling of stellar physical priors and historical flare time-series data within a Transformer architecture, while integrating heterogeneous multi-source features. Evaluated on the Kepler light-curve dataset, the model consistently outperforms existing approaches across all metrics. Real-world case studies further demonstrate its high reliability and strong generalization capability. This work fills a critical gap in dedicated flare prediction models and provides a novel tool for exoplanet habitability assessment and stellar activity research.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Diffusion Models for Vision

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web search models and ranking

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.

Problem

• Developing a stellar flare forecasting model.
• Integrating stellar properties and historical records.
• Enhancing astronomical research with additional flare data.

Innovation

• Integrates stellar properties
• Uses historical flare records
• Novel Soft Prompt Module