Vertex finding in neutrino-nucleus interaction: A Model Architecture Comparison

TL;DR

This study compares manually designed and automatically generated neural network architectures for vertex detection in neutrino interactions, finding similar performance and highlighting the efficiency of automated methods.

Contribution

It demonstrates that automated neural network architecture optimization can match expert-designed models, saving time and resources in neutrino vertex detection tasks.

Findings

Automated architectures perform comparably to hand-tuned models.

Systematic differences between models are minimal.

Automated optimization is a resource-efficient alternative.

Abstract

We compare different neural network architectures for Machine Learning (ML) algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package "Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a similar performance which suggests that the systematics associated with the optimized network architecture are small. Furthermore, we find that while the domain expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.