Speaker
Description
Project 8 is a next-generation neutrino mass experiment that uses Cyclotron Radiation Emission Spectroscopy (CRES) to measure the neutrino mass. CRES is a novel technique for $\beta$-decay spectroscopy that measures the frequency of the cyclotron radiation produced by energetic electrons trapped in a magnetic field. The cyclotron frequency can be directly converted into the energy spectrum, which yields the neutrino mass through measurement of the spectrum endpoint. The next phase of Project 8 seeks to measure the energy spectrum of molecular Tritium $\beta$- decay in an O(10 cm$^3$) free space volume, using a multi-channel phased array of antennas. The low signal power (< 1fW) and multi-channel reconstruction place stringent constraints on the triggering and online signal processing algorithms. I present progress on a machine learning triggering algorithm that employs a Deep Convolutional Neural Network (DCNN) to detect the presence of cyclotron radiation signals buried in noise. The network achieves greater than 85% classification accuracy on simulated electron signals, outperforming conventional methods, and rivaling the performance of an optimal matched filter while requiring significantly fewer computational resources for online operation.
