Nonlinear pile-up separation with LSTM neural networks for cryogenic particle detectors
This addresses data loss in high-background or calibration measurements for cryogenic particle detector applications, but appears incremental as it applies an existing LSTM method to a specific domain problem.
The paper tackled the problem of pile-up events in cryogenic particle detectors, which cause significant exposure loss, by proposing an LSTM neural network method that reconstructs the ground truth of a distorted energy spectrum reasonably well, despite a non-linear detector response.
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.