This paper is concerned with the automatic analysis of data coming from the multidetector array CHIMERA, used in nuclear physics at intermediate energies. Each of Chimera's detection cells is a telescope made of a ΔE silicon detector and a CsI(Tl) crystal, thick enough to stop all the charged light particles. The signals produced in the CsI(Tl) scintillators can he subdivided into two components-Fast and Slow. These data are collected in the form of bi-dimensional matrices (Fast-Slow matrices), particularly important for light particle identification. The proposed approach consists in applying image processing techniques. In particular, Grossberg's pre-attentive neural networks are used as a first step in order to isolate the regions of physical interest in the matrices and to roughly identify the directions depicted by the most intense lines; a successive step of filtering based on Markov random fields is then performed.

Processing CsI(Tl) 2D Matrices by means of neural network and Markov Random Fields

LANZALONE, GAETANO
Membro del Collaboration Group
;
2002

Abstract

This paper is concerned with the automatic analysis of data coming from the multidetector array CHIMERA, used in nuclear physics at intermediate energies. Each of Chimera's detection cells is a telescope made of a ΔE silicon detector and a CsI(Tl) crystal, thick enough to stop all the charged light particles. The signals produced in the CsI(Tl) scintillators can he subdivided into two components-Fast and Slow. These data are collected in the form of bi-dimensional matrices (Fast-Slow matrices), particularly important for light particle identification. The proposed approach consists in applying image processing techniques. In particular, Grossberg's pre-attentive neural networks are used as a first step in order to isolate the regions of physical interest in the matrices and to roughly identify the directions depicted by the most intense lines; a successive step of filtering based on Markov random fields is then performed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/7367
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