基于密度的CYGNO数据分析聚类算法
A density-based clustering algorithm for the CYGNO data analysis
摘要：
时间投影室（TPC）与气体电子倍增器（GEM）结合工作，产生能够观察低能事件的非常灵敏的探测器。这是通过高分辨率相机捕获GEM电子倍增过程中产生的光子来实现的。CYGNO实验最近开发了一种TPC Triple GEM探测器，该探测器与低噪声和高空间分辨率CMOS传感器耦合。对于图像分析，实现了一种基于众所周知的DBSCAN的改编版本的算法，称为iDBSCAN。在本文中给出的iDBSCAN算法的描述，包括测试和验证其参数，并与DBSCAN本身和一个广泛使用的算法被称为最近邻聚类（NNC）的比较。结果表明，DBSCAN的适应版本是能够提供完整的信号检测效率和非常好的能量分辨率，同时提高检测器的背景抑制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
