  Deep convolutional neural networks (DCNs) are a promising machine learning
technique to reconstruct events recorded by imaging atmospheric Cherenkov
telescopes (IACTs), but require optimization to reach full performance. One of
the most pressing challenges is processing raw images captured by cameras made
of hexagonal lattices of photo-multipliers, a common layout among IACT cameras
which topologically differs from the square lattices conventionally expected,
as their input data, by DCN models. Strategies directed to tackle this
challenge range from the conversion of the hexagonal lattices onto square
lattices by means of oversampling or interpolation to the implementation of
hexagonal convolutional kernels. In this contribution we present a comparison
of several of those strategies, using DCN models trained on simulated IACT
data.
