Title: Binary Classification of Light and Dark Time Traces of a Transition Edge Sensor Using Convolutional Neural Networks Show Chinese title

Title: 使用卷积神经网络对过渡边缘传感器的明暗时间痕迹进行二分类

Abstract: The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based detection scheme, cryogenic transition edge sensor (TES) based single-photon detectors are planned to expand the detection system for cross-checking the potential signals, for which a sensitivity on the order of $10^{-24}$ W is required. In order to reach this goal, we have investigated the use of convolutional neural networks (CNN) as binary classifiers to distinguish the experimentally measured 1064 nm photon triggered (light) pulses from background (dark) pulses. Despite extensive hyperparameter optimization, the CNN based binary classifier did not outperform our previously optimized cut-based analysis in terms of detection significance. This suggests that the used approach is not generally suitable for background suppression and improving the energy resolution of the TES. We partly attribute this to the training confusion induced by near-1064 nm black-body photon triggers in the background, which we identified as the limiting background source as concluded in our previous works. However, we argue that the problem ultimately lies in the binary classification based approach and believe that regression models would be better suitable for addressing the energy resolution. Unsupervised machine learning models, in particular neural network based autoencoders, should also be considered potential candidates for the suppression of noise in time traces. While the presented results and associated conclusions are obtained for TES designed to be used in the ALPS II experiment, they should hold equivalently well for any device whose output signal can be considered as a univariate time trace. Show Chinese abstract

Abstract: 任何光粒子搜索II（ALPS II）是一项通过墙的光实验，使用1064纳米激光源探测轴子和轴子样粒子的存在。 虽然ALPS II已经使用基于外差检测方案的数据采集，但计划使用基于低温过渡边缘传感器（TES）的单光子探测器来扩展检测系统，以交叉检查潜在信号，为此需要大约$10^{-24}$W的灵敏度。 为了达到这个目标，我们研究了卷积神经网络（CNN）作为二元分类器，以区分实验测量的1064纳米光子触发（光）脉冲与背景（暗）脉冲。 尽管进行了广泛的超参数优化，基于CNN的二元分类器在检测显著性方面并未优于我们之前优化的基于切割的分析方法。 这表明所使用的方法并不适用于背景抑制和提高TES的能量分辨率。 我们部分归因于背景中接近1064纳米黑体光子触发引起的训练混淆，我们在以前的工作中已将其确定为限制性背景源。 然而，我们认为问题最终在于基于二元分类的方法，并认为回归模型更适合解决能量分辨率问题。 特别是基于神经网络的自编码器的无监督机器学习模型也应被视为时间迹噪声抑制的潜在候选者。 虽然所呈现的结果和相关结论是针对设计用于ALPS II实验的TES获得的，但它们对于任何输出信号可被视为单变量时间迹的设备也应该同样适用。