RT Journal Article
T1 Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method
A1 Bussons Gordo, Javier
A1 Fernández Ruiz, Mario
A1 Prieto Mateo, Manuel
A1 Alvarado Díaz, Jorge
A1 Chávez de la O, Francisco
A1 Monstein, Christian
A1 Hidalgo Pérez, José Ignacio
AB 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.
PB Springer Nature
SN 0038-0938
SN 1573-093X
YR 2023
FD 2023-06
LK https://hdl.handle.net/20.500.14352/117342
UL https://hdl.handle.net/20.500.14352/117342
LA eng
DS Docta Complutense
RD 24 abr 2025