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 1 January 2018 to 22 August 2025, 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 CNN catalog shows a steeper power-law index for raw peak fluxes (-2.59 -\+ 0.02) than GOES (-2.25 -\+ 0.04), indicating the CNN's higher sensitivity to small events. After background correction, the indices are -1.97 -\+ 0.02 (CNN) and -2.05 -\+ 0.04 (GOES). The CNN catalog extends the power-law distribution of flare peak fluxes by one order of magnitude at the small-flux end compared with the GOES background-subtracted catalog. A Bayesian blocks analysis of the waiting-time distributions from the GOES and CNN catalogs indicates broad consistency with a piecewise Poisson process. We find that previously reported correlations between flare sizes and waiting times are significantly influenced by obscuration, that is, under-counting weaker or overlapping flares during periods of elevated flux. The new CNN catalog provides a foundation for complete and consistent studies of solar flare statistics.