Real-time Signal Detection for Cyclotron Radiation Emission Spectroscopy Measurements using Antenna Arrays

TL;DR

This paper compares signal detection algorithms for antenna array-based Cyclotron Radiation Emission Spectroscopy, demonstrating that advanced methods like matched filtering and machine learning can significantly improve neutrino mass measurement sensitivity.

Contribution

The study develops and compares efficiency models for three signal detection algorithms in antenna-based CRES, highlighting the benefits of advanced methods over traditional power threshold techniques.

Findings

Matched filter and machine learning approaches improve detection efficiency.

Enhanced algorithms increase neutrino mass sensitivity.

Moderate computational cost increase with advanced methods.

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 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.