Τίτλος:
Solar Energetic Particle Event occurrence prediction using Solar Flare
Soft X-ray measurements and Machine Learning
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
The prediction of the occurrence of Solar Energetic Particle (SEP)
events has been investigated over many years, and multiple works have
presented significant advances in this problem. The accurate and timely
prediction of SEPs is of interest to the scientific community as well as
mission designers, operators, and industrial partners due to the threat
SEPs pose to satellites, spacecrafts, and crewed missions. In this work,
we present a methodology for the prediction of SEPs from the soft X-rays
of solar flares associated with SEPs that were measured in 1 AU. We use
an expansive dataset covering 25 years of solar activity, 1988-2013,
which includes thousands of flares and more than two hundred identified
and catalogued SEPs. Neural networks are employed as the predictors in
the model, providing probabilities for the occurrence or not of a SEP,
which are converted to yes/no predictions. The neural networks are
designed using current and state-of-the-art tools integrating recent
advances in the machine learning field. The results of the methodology
are extensively evaluated and validated using all the available data,
and it is shown that we achieve very good levels of accuracy with
correct SEP occurrence prediction higher than 85% and correct no-SEP
predictions higher than 92%. Finally, we discuss further work towards
potential improvements and the applicability of our model in real-life
conditions.
Συγγραφείς:
Aminalragia-Giamini, Sigiava
Raptis, Savvas
Anastasiadis,
Anastasios
Tsigkanos, Antonis
Sandberg, Ingmar
Papaioannou,
Athanasios
Papadimitriou, Constantinos
Jiggens, Piers
Aran,
Angels
Daglis, Ioannis A.
Περιοδικό:
Journal of Space Weather and Space Climate
Εκδότης:
EDP SCIENCES S A
Λέξεις-κλειδιά:
Solar Energetic Particle Event; Solar Flare; Prediction; Machine
Learning
DOI:
10.1051/swsc/2021043
