Data reduction for a calorimetrically measured $^{163}\mathrm{Ho}$ spectrum of the ECHo-1k experiment

TL;DR

This paper presents a data reduction scheme for the ECHo-1k experiment that effectively filters noise and pile-up events in high-statistics $^{163}$Ho electron capture data, crucial for neutrino mass measurement.

Contribution

The paper introduces a two-level data filtering protocol that achieves over 99.8% efficiency in discarding non-signal events with minimal loss of true $^{163}$Ho events, enhancing spectral analysis accuracy.

Findings

Event filtering efficiency exceeds 99.8%.

Minimal loss (~0.7%) of true $^{163}$Ho events.

Energy-independent trigger time filter improves data quality.

Abstract

The electron capture in $^{163}\mathrm{Ho}$ experiment (ECHo) is designed to directly measure the effective electron neutrino mass by analysing the endpoint region of the $^{163}\mathrm{Ho}$ electron capture spectrum. We present a data reduction scheme for the analysis of high statistics data acquired with the first phase of the ECHo experiment, ECHo-1k, to reliably infer the energy of $^{163}\mathrm{Ho}$ events and discard triggered noise or pile-up events. On a first level, the raw data is filtered purely based on the trigger time information of the acquired signals. On a second level, the time profile of each triggered event is analysed to identify the signals corresponding to a single energy deposition in the detector. We demonstrate that events not belonging to this category are discarded with an efficiency above 99.8%, with a minimal loss of $^{163}\mathrm{Ho}$ events of about 0.7%. While the filter using the trigger time information is completely energy independent, a slight energy dependence of the filter based on the time profile is precisely characterised. This data reduction protocol will be important to minimise systematic errors in the analysis of the $^{163}\mathrm{Ho}$ spectrum for the determination of the effective electron neutrino mass.