2026-06-27T10:48:19Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1173422025-01-31T00:54:40Zcom_20.500.14352_14col_20.500.14352_15

Bussons Gordo, Javier Fernández Ruiz, Mario Prieto Mateo, Manuel Alvarado Díaz, Jorge Chávez de la O, Francisco Monstein, Christian Hidalgo Pérez, José Ignacio 2025-01-30T14:51:11Z 2025-01-30T14:51:11Z 2023-06 0038-0938 1573-093X 10.1007/s11207-023-02171-0 https://hdl.handle.net/20.500.14352/117342 https://link.springer.com/article/10.1007/s11207-023-02171-0 We present in detail an automatic radio-burst detection system, based on the AlexNet convolutional neural network, for use with any kind of solar spectrogram. A full methodology for model training, performance evaluation, and feedback to the model generator has been developed with special emphasis on i) robustness tests against stochastic and overfitting effects, ii) specific metrics adapted to the unbalanced nature of the solar-burst scenario, iii) tunable parameters for probability-threshold optimization, and iv) burst-coincidence cross match among e-Callisto stations and with external observatories (NOAA-SWPC). The resulting neural network configuration has been designed to accept data from observatories other than e-Callisto, either ground- or spacecraft-based. Typical False Negative and False Positive Scores in single-observatory mode are, respectively, in the 10 – 16% and 6 – 8% ranges, which improve further in cross-match mode. This mode includes new services (deARCE, Xmatch) allowing the end-user to check at a glance if a solar radio burst has taken place with a high level of confidence. eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 International Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method journal article