  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 the
capability of an antenna-based CRES experiment to measure the neutrino mass
depends on the efficiency of the signal detection algorithms. In this paper, we
develop efficiency models for three signal detection algorithms and compare
them using simulations from a prototype antenna-based CRES experiment as a
case-study. The algorithms include a power threshold, a matched filter template
bank, and a neural network based machine learning approach, which are analyzed
in 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 signal
detection algorithm used in previous CRES experiments by Project 8.

Instrumentation and Detectors;Nuclear Experiment