Signal processing and spectral modeling for the BeEST experiment

TL;DR

The paper details the development of advanced signal processing and spectral modeling techniques for the BeEST experiment, which aims to detect heavy neutrino states through nuclear decay measurements, utilizing a scaled sensor array and improved data acquisition.

Contribution

It introduces robust, automated spectral fitting procedures tailored for large data sets in the BeEST experiment's Phase-III, enhancing sensitivity to new physics.

Findings

Successful scaling to a 36-pixel sensor array

Development of automated spectral fitting procedures

Preparation for unblinded data analysis to search for new physics

Abstract

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.