Speaker
Description
The aim of the Beryllium Electron capture in Superconducting Tunnel junction (BeEST) experiment is to identify theorized heavy neutrino mass eigenstates. This is investigated via the electron capture (EC) decay of $^{7}$Be by measuring the recoil energy spectrum of the $^{7}$Li product with superconducting tunnel junction (STJ) sensors. To enable precise modeling of the decay's spectral characteristics, large amounts of data are needed. In the BeEST Phase-III, data was collected via hours-long measurements taken in many separate sessions. Consequently, slight differences in experimental conditions may be propagated into the datasets themselves. In this work, we present on statistical methods used to identify and correct (where possible) disagreements in spectral calibration and energy resolution for these datasets; this is done primarily by conducting statistical tests on the pulse shapes as well as the spectral peak produced by K-capture into the ground state of $^{7}$Li measured in different experiments. Intra-experiment testing is also undertaken to search for temporally dependent degradation effects. The results allow us to restore the viability of some miscalibrated datasets and provide a statistical basis for which sets to exclude from model fitting.