Application of deep learning to the evaluation of goodness in the waveform processing of transition-edge sensor calorimeters

TL;DR

This paper introduces a neural network method for automatically assessing the quality of TES calorimeter waveforms, improving data filtering efficiency without needing bad data for training.

Contribution

It presents a novel neural network approach for automatic goodness tagging of TES pulses, enhancing data analysis in calorimeter experiments.

Findings

Neural network accurately tags good and bad TES pulses.

Method improves data filtering speed and reliability.

No need for bad data in training process.

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.