Application of Deep Learning to the Evaluation of Goodness in the Waveform Processing of Transition-Edge Sensor Calorimeters
Application of Deep Learning to the Evaluation of Goodness in the Waveform Processing of Transition-Edge Sensor Calorimeters
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DOI：
10.1007/s10909-022-02719-7
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发表时间：
2022-04-18
影响因子：
2
通讯作者：
Okumura, T.
中科院分区：
文献类型：
--
作者：
Ichinohe, Y.;Yamada, S.;Okumura, T.
最佳滤波是过渡边缘传感器（TES）热量计数据分析的关键技术，以实现其最先进的能量分辨率。从数据集中过滤掉“坏”数据很重要，因为它会导致能量分辨率的下降，而这不是一项微不足道的任务。我们提出了一种基于神经网络的技术，用于TES脉冲的自动良好标记，该技术快速，自动，并且不需要坏数据进行训练。
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.