[P-EM10] Space Weather and Space Climate

[PEM10-P03] Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations

*Jargalmaa Batmunkh¹, Yusuke Iida¹, Takayoshi Oba² (1.Niigata University, 2.Max Planck Institute for Solar System Research)

Keywords:Solar physics, Sunspot, Spectro-polarimetry, Deep learning, Anomaly detection

Detecting unusual signals in observational solar spectra is crucial for understanding the features associated with impactful space weather events, such as solar flares. However, existing spectral analysis techniques face challenges, particularly when relying on pre-defined, physics-based calculations to process large volumes of noisy and complex observational data. To address these limitations, we applied deep learning to detect anomalies in the Stokes V spectra from the Hinode/SP instrument. Specifically, we developed an autoencoder model tailored for anomaly detection in pre-flare data. The model effectively identifies anomalous spectra within spectro-polarimetric maps captured prior to the onset of the X1.3 flare on May 5, 2024, in NOAA AR 13663. These atypical spectral points exhibit highly complex profiles and spatially align with polarity inversion lines in magnetogram images, indicating their potential as sites of magnetic energy storage and possible triggers for flares. Notably, the detected anomalies are highly localized, making them particularly challenging to identify in magnetogram images using current manual methods. These results suggest that the proposed approach holds promise as an automated, complementary detection tool to support solar flare prediction.