Time-domain Anomalies in Solar and Stellar Flares
太阳与恒星耀斑中的时域异常
The temporal morphology of flare light curves encodes the underlying flare physics, and deviations from the typical flare profile may indicate the presence of mechanisms not captured by a standard flare model. To search for such time-domain deviations from a "standard" flare, we develop an unsupervised deep-support-vector data-description model, which learns a compact representation of normal flares, against which unseen anomalous flares are identified. The model is trained on synthetic light curves with a "normal" flare morphology, generated from existing analytical flare-trend models with noise. Using the distribution of normal flare data, we introduce a probabilistic flare anomaly index (FLAI) which allows for separating flare light curves into three distinct classes: normal data (ND), weak anomalies (WAs), and strong anomalies (SAs). Application of the FLAI to the Kepler flare catalogue (white light) reveals that 36% and 30% of events belong to the WA and SA classes, respectively. For M- and X-class solar flares from the STIX flare list, 25% and 32% of events in the 15─25 keV channel are classified as WAs and SAs, respectively, versus 15% for both WA and SA classes in the 4─10 keV channel. Thus, anomalous flares appear more frequently in the STIX high-energy channel. These results show that both solar and stellar flares often deviate from the normal flare population used for model training, suggesting departures from the standard flare scenario, such as modified energy release and dissipation, or the development of wave and oscillatory processes in flare sites.
展开 ▾提出耀斑异常指数(FLAI)将耀斑分为正常、弱异常和强异常三类,揭示标准耀斑模型未能涵盖的多样物理过程。
标准耀斑模型假设光变曲线具有典型快速上升和指数衰减轮廓,但观测中常发现偏离,表明存在未被标准模型捕获的机制。本研究利用深度学习方法从合成正常耀斑中学习紧凑特征表示,通过异常指数量化时域偏差,应用于开普勒和STIX数据证实了偏差的普遍性。未来该方法可推广至不同波段和时间尺度的耀斑,帮助探索修正的能量释放、耗散过程或波动振荡现象,并与不同恒星类型的耀斑特性进行对比研究。
接收 2026-06-22 · 刊出 2026-07-17 · 收录 2026-07-28