A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX

TL;DR

This paper presents a neural network-based method for accurately estimating solar flare locations from X-ray data on the Solar Orbiter, optimized for onboard computational constraints, outperforming existing algorithms.

Contribution

The paper introduces a neural network model with post-training quantization for efficient, accurate solar flare location estimation onboard Solar Orbiter, improving over current methods.

Findings

Neural network outperforms existing algorithms in accuracy.

Model requires fewer parameters and uses integer arithmetic.

Potential for real-time onboard flare localization.

Abstract

The Spectrometer/Telescope for Imaging X-rays (STIX) on-board the ESA Solar Orbiter mission retrieves the coordinates of solar flare locations by means of a specific sub-collimator, named the Coarse Flare Locator (CFL). When a solar flare occurs on the Sun, the emitted X-ray radiation casts the shadow of a peculiar "H-shaped" tungsten grid over the CFL X-ray detector. From measurements of the areas of the detector that are illuminated by the X-ray radiation, it is possible to retrieve the $(x,y)$ coordinates of the flare location on the solar disk. In this paper, we train a neural network on a dataset of real CFL observations to estimate the coordinates of solar flare locations. Further, we apply a post-training quantization technique specifically tailored to the adopted model architecture. This technique allows all computations to be in integer arithmetic at inference time, making the model compatible with the STIX computational requirements. We show that our model outperforms the currently adopted algorithm for estimating the flare locations from CFL data regarding prediction accuracy while requiring fewer parameters. We finally discuss possible future applications of the proposed model on-board STIX.