Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields.
Jun 12, 2021·
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Nicolò Pinciroli
Ibrahim A. Hameed
Michael Kachelriess
Abstract
The presence of non-zero helicity in intergalactic magnetic fields is a smoking gun for their primordial origin since they have to be generated by processes that break CP invariance. As an experimental signature for the presence of helical magnetic fields, an estimator Q based on the triple scalar product of the wave-vectors of photons generated in electromagnetic cascades from, e.g., TeV blazars, has been suggested previously. We propose to apply deep learning to helicity classification employing Convolutional Neural Networks and show that this method outperforms the Q estimator.
Type
Publication
Proceedings of Science - ICRC
Authors
Nicolò Pinciroli
(he/him)
PhD student in Information Technology
I am a PhD student in Information Technology at Politecnico di Milano. My research focuses on machine learning and data science applications to astrophysics problems, particularly in the search for pulsars and the analysis of gravitational lenses. I have experience in AI, data analysis, and computer vision.
Authors
Authors