# Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields

arXiv: https://arxiv.org/abs/2106.06718 · HTML: https://mlsanity.com/paper/2106.06718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [A Data Set and a Convolutional Model for Iconography Classification in Paintings](https://mlsanity.com/paper/2010.11697/) (2020)

## Abstract (arXiv)

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.