John Cesar | Searcharxiv

arXiv2022

Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors

This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network have been trained to reject cosmic muon events, classify beam events, and estimate neutrino energies, using only a slightly modified version of the raw detector event as input. When evaluated on a realistic selection of simulated CHIPS-5kton prototype detector events, this new approach significantly increases performance over the standard likelihood-based reconstruction and simple neural network classification.

hep-ex↗

arXiv2021

Low-latency NuMI Trigger for the CHIPS-5 Neutrino Detector

The CHIPS R&D project aims to develop affordable large-scale water Cherenkov neutrino detectors for underwater deployment. In 2019, a 5kt prototype detector CHIPS-5 was deployed in northern Minnesota to potentially study neutrinos generated by the NuMI beam. This paper presents the dedicated low-latency triggering system for CHIPS-5 that delivers notifications of neutrino spills from the Fermilab accelerator complex to the detector with sub-nanosecond precision. Building on existing NOvA infrastructure, the time distribution system achieves this using only open-source software and conventional computing and network elements. In a time-of-flight study, the system reliably provided advance notifications $610 \pm 330\text{ ms}$ prior to neutrino spills at 96% efficiency. This permits advanced analysis in real-time as well as hardware-assisted triggering that saves data bandwidth and reduces DAQ computing load outside time windows of interest.

physics.ins-det↗