@Article{Balibrea-Correa_etal2021,
author="Balibrea-Correa, J.
and Lerendegui-Marco, J.
and Babiano-Suarez, V.
and Caballero, L.
and Calvo, D.
and Ladarescu, I.
and Olleros-Rodriguez, P.
and Domingo-Pardo, C.",
title="Machine Learning aided 3D-position reconstruction in large LaCl3 crystals",
journal="Nuclear Instruments {\&} Methods in Physics Research A",
year="2021",
publisher="Elsevier",
volume="1001",
pages="165249--17pp",
optkeywords="Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section",
abstract="We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70{\%}-80{\%} of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.",
optnote="WOS:000641308300007",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=4803), last updated on Thu, 20 May 2021 09:12:00 +0000",
issn="0168-9002",
doi="10.1016/j.nima.2021.165249",
opturl="https://arxiv.org/abs/2010.13427",
opturl="https://doi.org/10.1016/j.nima.2021.165249",
language="English"
}