论文标题
一种基于密度的聚类算法，用于CYGNO数据分析
A density-based clustering algorithm for the CYGNO data analysis
论文作者
论文摘要
时间投影室（TPC）与气体电子倍增器（GEM）结合使用，产生了一个非常敏感的检测器，能够观察低能事件。这是通过通过高分辨率摄像头捕获在宝石电子乘法过程中生成的光子来实现的。 Cygno实验最近开发了一个TPC三重GEM探测器，该探测器与低噪声和高空间分辨率CMOS传感器相结合。对于图像分析，实施了基于著名DBSCAN的改编版本的算法，称为IDBSCAN。在本文中，给出了IDBSCAN算法的描述，包括对其参数的测试和验证，以及与DBSCAN本身的比较以及一种被称为最近的邻居聚类（NNC）的广泛使用的算法。结果表明，DBSCAN的改编版能够提供完整的信号检测效率和良好的能量分辨率，同时改善检测器背景拒绝。
Time Projection Chambers (TPCs) working in combination with Gas Electron Multipliers (GEMs) produce a very sensitive detector capable of observing low energy events. This is achieved by capturing photons generated during the GEM electron multiplication process by means of a high-resolution camera. The CYGNO experiment has recently developed a TPC Triple GEM detector coupled to a low noise and high spatial resolution CMOS sensor. For the image analysis, an algorithm based on an adapted version of the well-known DBSCAN was implemented, called iDBSCAN. In this paper a description of the iDBSCAN algorithm is given, including test and validation of its parameters, and a comparison with DBSCAN itself and a widely used algorithm known as Nearest Neighbor Clustering (NNC). The results show that the adapted version of DBSCAN is capable of providing full signal detection efficiency and very good energy resolution while improving the detector background rejection.
