  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.

Instrumentation and Detectors;Machine Learning