论文标题
深度学习在过渡 - 边缘传感器热量计的波形处理中的评估中的应用
Application of deep learning to the evaluation of goodness in the waveform processing of transition-edge sensor calorimeters
论文作者
论文摘要
最佳过滤是过渡 - 传感器（TES）量热计的数据分析以实现其最先进的能量分辨率的关键技术。从数据集中滤除“不良”数据很重要，因为否则它会导致能量分辨率的降解，而这并不是一项琐碎的任务。我们提出了一种基于神经网络的技术，用于对TES脉冲的自动优点标记，该技术是快速而自动的，并且不需要训练的数据不好。
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.
