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Author  |
Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C. |
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Title |
Machine Learning aided 3D-position reconstruction in large LaCl3 crystals |
Type |
Journal Article |
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Year |
2021 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
1001 |
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Pages |
165249 - 17pp |
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Keywords |
Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section |
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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. |
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Address |
[Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: javier.balibrea@ific.uv.es |
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Publisher |
Elsevier |
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Language |
English |
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Original Title |
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Series Volume |
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ISSN |
0168-9002 |
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Notes |
WOS:000641308300007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
4803 |
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