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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.
The paper reviews recent progress in particle identification methods. A survey of motivations and requirements for particle identification in various experimental environments is followed by the main emphasis, which is on the recent development of Ch
Equipping an experiment at FCC-ee with particle identification (PID) capabilities, in particular the ability to distinguish between hadron species, would bring great benefits to the physics programme. Good PID is essential for precise studies in quar
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JUNO is a multi-purpose neutrino experiment currently under construction in Jiangmen, China. It is primary aiming to determine the neutrino mass ordering. Moreover, its 20,kt target mass makes it an ideal detector to study neutrinos from various sour