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

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