  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.
