A density-based clustering algorithm for the CYGNO data analysis

E. Baracchini; L. Benussi; S. Bianco; C. Capoccia; M. Caponero; G. Cavoto; A. Cortez; I. A. Costa; E. Di Marco; G. D'Imperio; G. Dho; F. Iacoangeli; G. Maccarrone; M. Marafini; G. Mazzitelli; A. Messina; R. A. Nobrega; A. Orlandi; E. Paoletti; L. Passamonti; F. Petrucci; D. Piccolo; D. Pierluigi; D. Pinci; F. Renga; F. Rosatelli; A. Russo; G. Saviano; S. Tomassini

doi:10.1088/1748-0221/15/12/t12003

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arXiv · 2007.01763

A density-based clustering algorithm for the CYGNO data analysis

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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.

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E. Baracchini, L. Benussi, S. Bianco, C. Capoccia, M. Caponero, G. Cavoto, A. Cortez, I. A. Costa, E. Di Marco, G. D'Imperio, G. Dho, F. Iacoangeli, G. Maccarrone, M. Marafini, G. Mazzitelli, A. Messina, R. A. Nobrega, A. Orlandi, E. Paoletti, L. Passamonti, F. Petrucci, D. Piccolo, D. Pierluigi, D. Pinci, F. Renga, F. Rosatelli, A. Russo, G. Saviano, S. Tomassini. 2020-07-03. A density-based clustering algorithm for the CYGNO data analysis. https://doi.org/10.1088/1748-0221/15/12/t12003

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