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The electron capture in $$^{163}\mathrm {Ho}$$ 163 Ho experiment (ECHo) is designed to directly measure the effective electron neutrino mass by analysing the endpoint region of the $$^{163}\mathrm {Ho}$$ 163 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 $$^{163}\mathrm {Ho}$$ 163 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 $$^{163}\mathrm {Ho}$$ 163 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 $$^{163}\mathrm {Ho}$$ 163 Ho spectrum for the determination of the effective electron neutrino mass.

The goal of the Electron Capture in Ho-163 (ECHo) experiment is the determination of the electron neutrino mass by analyzing the electron capture (EC) spectrum of Ho-163. Metallic magnetic calorimeters operated at low temperatures, in which the Ho-163 has been implanted, have been selected to conduct a high resolution, high statistics, and low background calorimetric measurement of the Ho-163 EC spectrum. In order to reliably infer the energy of Ho-163 events and discard triggered noise or pile-up events, fast and robust analysis algorithms are required. We present the results obtained by applying the analysis algorithms to different data files affected in a different manner by noise sources. Based on these results, we then discuss the present structure for data reduction algorithms to be applied for the analysis of the ECHo-1k high statistics data.