http://arxiv.org/abs/2204.11474
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.
Y. Ichinohe, S. Yamada, R. Hayakawa, et. al.
Tue, 26 Apr 22
62/74
Comments: Proceedings of the LTD19 conference