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

TL;DR

This paper introduces an LSTM neural network method to effectively separate pile-up events in cryogenic particle detectors, improving energy spectrum reconstruction despite non-linear detector responses.

Contribution

The study presents a novel application of LSTM neural networks for pile-up separation in cryogenic detectors, handling non-linear responses for the first time.

Findings

LSTM-based method accurately separates pile-up events.

Reconstructs distorted energy spectra effectively.

Performs well despite non-linear detector responses.

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.