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

arXiv: https://arxiv.org/abs/2101.11550 · HTML: https://mlsanity.com/paper/2101.11550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics](https://mlsanity.com/paper/1905.13575/) (2019)
- [Solar Image Deconvolution by Generative Adversarial Network](https://mlsanity.com/paper/2001.03850/) (2020)
- [Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning](https://mlsanity.com/paper/1911.04008/) (2019)

## Built on by (most cited first, 2 of 2)

- [Example-Based Explainable AI and its Application for Remote Sensing Image Classification](https://mlsanity.com/paper/2302.01526/) (2023)
- [Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach](https://mlsanity.com/paper/2306.08270/) (2023)

## Abstract (arXiv)

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.