%0 Journal Article
%T Machine Learning aided 3D-position reconstruction in large LaCl3 crystals
%A Balibrea-Correa, J.
%A Lerendegui-Marco, J.
%A Babiano-Suarez, V.
%A Caballero, L.
%A Calvo, D.
%A Ladarescu, I.
%A Olleros-Rodriguez, P.
%A Domingo-Pardo, C.
%J Nuclear Instruments & Methods in Physics Research A
%D 2021
%V 1001
%I Elsevier
%@ 0168-9002
%G English
%F Balibrea-Correa_etal2021
%O WOS:000641308300007
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=4803), last updated on Thu, 20 May 2021 09:12:00 +0000
%X 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.
%K Gamma-ray
%K Position sensitive detectors
%K Monolithic crystals
%K Compton imaging
%K Machine Learning
%K Convolutional Neural Networks
%K Total Energy Detector
%K Neutron capture cross-section
%R 10.1016/j.nima.2021.165249
%U https://arxiv.org/abs/2010.13427
%U https://doi.org/10.1016/j.nima.2021.165249
%P 165249-17pp