  This work proposes a domain-informed neural network architecture for
experimental particle physics, using particle interaction localization with the
time-projection chamber (TPC) technology for dark matter research as an example
application. A key feature of the signals generated within the TPC is that they
allow localization of particle interactions through a process called
reconstruction. While multilayer perceptrons (MLPs) have emerged as a leading
contender for reconstruction in TPCs, such a black-box approach does not
reflect prior knowledge of the underlying scientific processes. This paper
looks anew at neural network-based interaction localization and encodes prior
detector knowledge, in terms of both signal characteristics and detector
geometry, into the feature encoding and the output layers of a multilayer
neural network. The resulting Domain-informed Neural Network (DiNN) limits the
receptive fields of the neurons in the initial feature encoding layers in order
to account for the spatially localized nature of the signals produced within
the TPC. This aspect of the DiNN, which has similarities with the emerging area
of graph neural networks in that the neurons in the initial layers only connect
to a handful of neurons in their succeeding layer, significantly reduces the
number of parameters in the network in comparison to an MLP. In addition, in
order to account for the detector geometry, the output layers of the network
are modified using two geometric transformations to ensure the DiNN produces
localizations within the interior of the detector. The end result is a neural
network architecture that has 60% fewer parameters than an MLP, but that still
achieves similar localization performance and provides a path to future
architectural developments with improved performance because of their ability
to encode additional domain knowledge into the architecture.
