PoS - Proceedings of Science
Volume 395 - 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect
Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields
N.O. Pinciroli Vago*, I. A. Hameed and M. Kachelriess
Full text: pdf
Pre-published on: July 04, 2021
Published on: March 18, 2022
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.
DOI: https://doi.org/10.22323/1.395.0906
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.