论文标题
CAIT：Python中的低温颗粒探测器的分析工具包
Cait: analysis toolkit for cryogenic particle detectors in Python
论文作者
论文摘要
低温固态检测器广泛用于暗物质和中微子实验，需要明智的原始数据分析。为此，我们提出了Cait，这是一种开源Python软件包，其所有基本方法用于分析与Python生态系统完全集成的检测器模块，以进行科学计算和机器学习。它采用了从连续采样流触发事件的方法，在低信噪比环境中鉴定粒子后坐力和伪影，沉积能量的重建以及多种典型事件类型的模拟。此外，通过将CAIT与现有的机器学习框架联系起来，我们引入了新方法，以更好地自动化数据清洁和背景拒绝。
Cryogenic solid state detectors are widely used in dark matter and neutrino experiments, and require a sensible raw data analysis. For this purpose, we present Cait, an open source Python package with all essential methods for the analysis of detector modules fully integrable with the Python ecosystem for scientific computing and machine learning. It comes with methods for triggering of events from continuously sampled streams, identification of particle recoils and artifacts in a low signal-to-noise ratio environment, the reconstruction of deposited energies, and the simulation of a variety of typical event types. Furthermore, by connecting Cait with existing machine learning frameworks we introduce novel methods, for better automation in data cleaning and background rejection.
