  The success of Convolutional Neural Networks (CNNs) in image classification
has prompted efforts to study their use for classifying image data obtained in
Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D
and 3D image data from particle physics experiments to classify signal from
background.
  In this work we present an extensive convolutional neural architecture
search, achieving high accuracy for signal/background discrimination for a HEP
classification use-case based on simulated data from the Ice Cube neutrino
observatory and an ATLAS-like detector. We demonstrate among other things that
we can achieve the same accuracy as complex ResNet architectures with CNNs with
less parameters, and present comparisons of computational requirements,
training and inference times.
