Nonlinear pile-up separation with LSTM neural networks for cryogenic particle detectors

Abstract

In high-background or calibration measurements with cryogenic particle detectors, a significant share of the exposure is lost due to pile-up of recoil events. We propose a method for the separation of pile-up events with an LSTM neural network and evaluate its performance on an exemplary data set. Despite a non-linear detector response function, we can reconstruct the ground truth of a severely distorted energy spectrum reasonably well.

Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)