A density-based clustering algorithm for the CYGNO data analysis

TL;DR

This paper introduces iDBSCAN, an adapted density-based clustering algorithm for analyzing high-resolution images from CYGNO's TPC detector, improving signal detection and background rejection.

Contribution

The paper presents a novel adaptation of DBSCAN, called iDBSCAN, optimized for CYGNO's TPC data analysis, with validation and comparison to existing algorithms.

Findings

iDBSCAN achieves full signal detection efficiency.

It provides very good energy resolution.

It improves background rejection capabilities.

Abstract

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.