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

arXiv: https://arxiv.org/abs/2112.06792 · HTML: https://mlsanity.com/paper/2112.06792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)

## Abstract (arXiv)

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.