Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields

TL;DR

This paper introduces a deep learning approach using Convolutional Neural Networks to classify the helicity of magnetic fields, demonstrating superior performance over traditional estimators in astrophysical data analysis.

Contribution

The study presents a novel application of CNNs for magnetic helicity classification, outperforming existing $Q$ estimator methods in astrophysical contexts.

Findings

CNN-based method outperforms $Q$ estimator in accuracy

Deep learning improves helicity detection in electromagnetic cascades

Method applicable to astrophysical magnetic field analysis

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.