  Convolutional Neural Networks (CNNs) have shown strong promise for analyzing
scientific data from many domains including particle imaging detectors.
However, the challenge of choosing the appropriate network architecture (depth,
kernel shapes, activation functions, etc.) for specific applications and
different data sets is still poorly understood. In this paper, we study the
relationships between a CNN's architecture and its performance by proposing a
systematic language that is useful for comparison between different CNN's
architectures before training time. We characterize CNN's architecture by
different attributes, and demonstrate that the attributes can be predictive of
the networks' performance in two specific computer vision-based physics
problems -- event vertex finding and hadron multiplicity classification in the
MINERvA experiment at Fermi National Accelerator Laboratory. In doing so, we
extract several architectural attributes from optimized networks' architecture
for the physics problems, which are outputs of a model selection algorithm
called Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL). We
use machine learning models to predict whether a network can perform better
than a certain threshold accuracy before training. The models perform 16-20%
better than random guessing. Additionally, we found an coefficient of
determination of 0.966 for an Ordinary Least Squares model in a regression on
accuracy over a large population of networks.
