Work

Scintillating light track reconstruction for fast neutron detection based on deep-learning techniques

Year: 2025

Type: conference-paper

Abstract: Imaging techniques based on tracking have progressed from manual examination to the utilization of contemporary photodetectors, like SiPM arrays and CMOS cameras, to convert scintillation light into digital data and obtain physical information. This study presents RIPTIDE, a novel recoil-proton track imaging system designed for fast neutron detection, with an emphasis on the use of deep-learning methods. RIPTIDE utilizes neutron-proton elastic scattering within a plastic scintillator to produce scintillation light, creating images that document scattering occurrences. A deep neural network is employed to rectify optical distortions in proton track images, enhancing their form and alignment. This adjustment improves the precision of track length measurements, which directly affects proton energy estimation and neutron kinematics reconstruction.

DOI: 10.22323/1.488.0021

Authors: Samuele Lanzi, Patrizio Console Camprini, Francesco GIACOMINI, Cristian Massimi, Alberto Mengarelli, Licia Mozzina, Claudia Pisanti, Riccardo Ridolfi, Roberto Spighi, Mauro Villa

Institutions: Istituto Nazionale di Fisica Nucleare, Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Istituto Nazionale di Fisica Nucleare, Centro Nazionale Analisi Fotogrammi, University of Bologna

Language: English

Topic: Radiation Detection and Scintillator Technologies

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4415359107

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