Application of deep learning to the evaluation of goodness in the waveform processing of transition-edge sensor calorimeters
arXiv:2204.11474 · doi:10.1007/s10909-022-02719-7
Abstract
Optimal filtering is the crucial technique for the data analysis of transition-edge-sensor (TES) calorimeters to achieve their state-of-the-art energy resolutions. Filtering out the `bad' data from the dataset is important because it otherwise leads to the degradation of energy resolutions, while it is not a trivial task. We propose a neural network-based technique for the automatic goodness tagging of TES pulses, which is fast and automatic and does not require bad data for training.
Proceedings of the LTD19 conference