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

2025-04-08

AI Summary

Hinode/SP Stokes V spectra suffer from low signal-to-noise ratio, spatially localized anomalies, and insufficient physical modeling, hindering reliable detection of subtle magnetic disturbances. Method: This paper proposes an unsupervised anomaly detection framework based on autoencoders—the first deep learning approach applied to solar polarimetric spectral anomaly identification. It learns typical spectral line profiles end-to-end, enabling automatic localization of weak deviations without prior physical assumptions or manual labeling. The method integrates spectro-polarimetric analysis with co-aligned magnetogram spatial registration for sub-pixel anomaly localization. Results: Applied to active region AR 13663, the method successfully identified pre-flare Stokes V anomalies concentrated near the magnetic polarity inversion line hours before the X1.3 flare on 5 May 2024. It achieves significantly higher detection accuracy and spatial resolution than conventional manual identification techniques.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Calibration & Uncertainty Quantification

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• Responsible Web: Machine-in-the-loop, human agency and autonomy
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Abstract

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.

Problem

• Detect anomalous Stokes V spectra in solar flare regions
• Overcome challenges in noisy, complex solar spectral data analysis
• Identify localized magnetic energy storage sites for flares

Innovation

• Autoencoder model for spectral compression
• Detects anomalies in Stokes V spectra
• Identifies flare-related magnetic energy sites