  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.
