  The goal of this work is to investigate the possibility of improving current
gamma/hadron discrimination based on their shower patterns recorded on the
ground. To this end we propose the use of Convolutional Neural Networks (CNNs)
for their ability to distinguish patterns based on automatically designed
features. In order to promote the creation of CNNs that properly uncover the
hidden patterns in the data, and at same time avoid the burden of hand-crafting
the topology and learning hyper-parameters we resort to NeuroEvolution; in
particular we use Fast-DENSER++, a variant of Deep Evolutionary Network
Structured Representation. The results show that the best CNN generated by
Fast-DENSER++ improves by a factor of 2 when compared with the results reported
by classic statistical approaches. Additionally, we experiment ensembling the
10 best generated CNNs, one from each of the evolutionary runs; the ensemble
leads to an improvement by a factor of 2.3. These results show that it is
possible to improve the gamma/hadron discrimination based on CNNs that are
automatically generated and are trained with instances of the ground impact
patterns.
