Automatic Detection of Occulted Hard X-ray Flares Using Deep-Learning Methods

TL;DR

This paper introduces a deep-learning method using convolutional neural networks to automatically identify occulted solar flares from RHESSI X-ray spectrograms, significantly reducing analysis time and expert intervention.

Contribution

It demonstrates the first application of CNNs for classifying occulted versus on-disk solar flares using spectrogram images, achieving over 90% accuracy.

Findings

Achieved classification accuracy >90%.

Enabled automatic detection without image reconstruction.

Demonstrated potential for real-time flare screening.

Abstract

We present a concept for a machine-learning classification of hard X-ray (HXR) emissions from solar flares observed by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), identifying flares that are either occulted by the solar limb or located on the solar disk. Although HXR observations of occulted flares are important for particle-acceleration studies, HXR data analyses for past observations were time consuming and required specialized expertise. Machine-learning techniques are promising for this situation, and we constructed a sample model to demonstrate the concept using a deep-learning technique. Input data to the model are HXR spectrograms that are easily produced from RHESSI data. The model can detect occulted flares without the need for image reconstruction nor for visual inspection by experts. A technique of convolutional neural networks was used in this model by regarding the input data as images. Our model achieved a classification accuracy better than 90 %, and the ability for the application of the method to either event screening or for an event alert for occulted flares was successfully demonstrated.