A Convolutional Neural Network–derived Catalog of Solar Flares from Soft X-Ray Observations

Abstract

A convolutional neural network (CNN) is used to construct a new catalog for solar flares based on high-resolution (1 s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data. The CNN is trained to identify flare rise episodes. From 2018 January 1 to 2025 August 22, the algorithm detects 111,580 flare candidates, compared with 14,612 events in the corresponding GOES catalog. For each candidate, the probability of being a true positive is quantified by Bayesian inference based on the peak flux, rise time, and temporal coincidence with cataloged events where available. The flare size and waiting-time distributions are studied and compared with the GOES catalog. The C..