About The CNN Catalog:

A new solar flare catalog is constructed using a convolutional neural network (CNN)–based detection framework. The method operates on high-resolution (1s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data and identifies flare rise episodes (rather than the complete rise–decay profiles). By focusing on the rise episode, the approach partially relaxes the slow-driving (non-overlap) constraints inherent to many conventional flare detection algorithms, thereby allowing consecutive and overlapping events, particularly those initiating during the decay of a preceding flare, to be recognized. From 01 January 2018 to 22 August 2025, the algorithm detected 111,580 flare candidates, compared with 14,612 events in the corresponding GOES archive. For each candidate, the probability of being a true positive is quantified by a Bayesian inference based on the peak flux, rise time, and temporal coincidence with known cataloged events where available.