PNNL

Abstract

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40 meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but to estimate the neutrino mass sensitivity achievable with a particular design, it is necessary to accurately model the efficiency of the signal detection algorithms. Therefore, efficiency models are developed for three signal detection algorithms and compared using simulations from a prototypical antenna array CRES experiment design as a case-study. The algorithms include a power threshold trigger, a matched filter template bank, and a neural network based machine learning approach, which are analyzed terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline triggering algorithm used in previous CRES experiments by Project 8.