Performance of solar far-side active regions neural detection

TL;DR

This study evaluates a neural network's ability to detect far-side solar active regions using helioseismic data, demonstrating improved sensitivity and detection rates over traditional methods, confirmed by EUV observations.

Contribution

The paper introduces and validates a neural network approach that enhances the detection sensitivity of far-side solar active regions compared to standard seismic methods.

Findings

Neural network shows 47% more true detections at similar false positive rates.

Approximately 96% of standard method detections correspond to EUV active regions.

Neural network can detect weaker active regions by adjusting detection thresholds.

Abstract

Context. Far-side helioseismology is a technique used to infer the presence of active regions in the far hemisphere of the Sun based on the interpretation of oscillations measured in the near hemisphere. A neural network has been recently developed to improve the sensitivity of the seismic maps to the presence of far-side active regions. Aims. Our aim is to evaluate the performance of the new neural network approach and to thoroughly compare it with the standard method commonly applied to predict far-side active regions from seismic measurements. Methods. We have computed the predictions of active regions using the neural network and the standard approach from five years of far-side seismic maps as a function of the selected threshold in the signatures of the detections. The results have been compared with direct extreme ultraviolet observations of the far hemisphere acquired with the Solar Terrestrial Relations Observatory (STEREO). Results. We have confirmed the improved sensitivity of the neural network to the presence of far-side active regions. Approximately 96% of the active regions identified by the standard method with a strength above the threshold commonly employed by previous analyses are related to locations with enhanced extreme ultraviolet emission. For this threshold, the false positive ratio is 3.75%. For an equivalent false positive ratio, the neural network produces 47% more true detections. Weaker active regions can be detected by relaxing the threshold in their seismic signature. Conclusions. The neural network is a promising approach to improve the interpretation of the seismic maps provided by local helioseismic techniques.