Particle identification using clustering algorithms

TL;DR

This paper introduces a generic, unsupervised fuzzy clustering method for particle identification based on pulse shape analysis, applicable across various detector types and demonstrated on scintillator signals.

Contribution

It presents a novel, assumption-free fuzzy c-means clustering approach for particle identification that outperforms traditional integration methods.

Findings

Effective discrimination between photon and proton signals

Method is generic and adaptable to different detectors

Comparable or improved accuracy over existing methods

Abstract

A method that uses fuzzy clustering algorithms to achieve particle identification based on pulse shape analysis is presented. The fuzzy c-means clustering algorithm is used to compute mean (principal) pulse shapes induced by different particle species in an automatic and unsupervised fashion from a mixed set of data. A discrimination amplitude is proposed using these principal pulse shapes to identify the originating particle species of a detector pulse. Since this method does not make any assumptions about the specific features of the pulse shapes, it is very generic and suitable for multiple types of detectors. The method is applied to discriminate between photon- and proton-induced signals in CsI(Tl) scintillator detectors and the results are compared to the well-known integration method.