Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations
This work addresses the challenge of detecting subtle, localized anomalies in noisy solar spectral data for solar physicists, though it is incremental as it applies an existing deep learning method to a new domain.
The researchers tackled the problem of detecting unusual signals in solar spectra to understand solar flares by applying deep learning, specifically an autoencoder model, to identify anomalous Stokes V spectra in data from the Hinode/SP instrument, effectively locating highly localized anomalies that align with polarity inversion lines prior to an X1.3 flare.
Detecting unusual signals in observational solar spectra is crucial for understanding the features associated with impactful solar 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 for spectral compression, which serves as an anomaly detection method. Our 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.