Mario Fernández Ruiz defends his Final Degree Project “Application of automatic detection of solar flares by e-Callisto with neural networks”, the result of a collaboration between the CELESTINA project (Castillian E-Callisto Leading Experimentation in Solar-Terrestrial Interaction with Novel Antennas, https://celestina.web.uah.es) and Micro Stress-MAP, http://absys.dacya.ucm.es/.
On July 23, 2021, Mario Fernández Ruiz, a student of the Degree in Mathematics at the University of Murcia, presented and defended with outstanding grade his Final Degree Project, tutored by the professor of the same university Javier Bussons Gordo, in which convolutional neural networks (CNN) are used for the first time in order to speed up and improve the detection of solar flares in the database of the international network of low-cost solar spectrometers e-Callisto, http://www.e-callisto.org/.
This network was created by Prof. Christian Monstein (Zurich Polytechnic Institute and Locarno Solar Research Institute) following the International Heliophysical Year (2007) under the auspices of UNOOSA (United Nations Office for Outer Space Affairs) and is part of the International Space Weather Initiative (ISWI,
https://www.unoosa.org/oosa/en/ourwork/psa/bssi/iswi.html).
In his work, Mario presents independent neural network training models for five of the best e-Callisto observing stations (Astronomical Society of South Australia, University of Glasgow, Trinity College Dublin’s Birr Observatory, Switzerland’s Heiterswil and Landschlacht observatories) making extensive use of CNN-type networks under the DIGITS architecture and NVIDIA processors made available by collaborators at UCM and UNEZ. http://bioinspired.dacya.ucm.es.
