Optimization of CeBr<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1577" altimg="si46.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math> position-sensitive calorimeter module

نویسندگان

چکیده

For the next generation of MeV range gamma-ray telescopes, position sensitive calorimeters based on a monolithic scintillator coupled to pixelated photodetector could be an important building block. In this paper, we present optimization reconstruction algorithms using machine learning, for detector 51×51×10mm3 CeBr3 crystal. that purpose, used automated test bench and collimated radioactive sources generate experimental data known energy by irradiating with gamma rays. We found in these different interaction morphologies which perform differently, developed algorithm automatically classify them. also conducted extensive artificial neural networks 3D Keras Python library Theano backend. at 662keV, 90% events have morphology facilitates reconstruction. The optimized give those rms error plane 1.8mm each axis. depth crystal is 2mm.

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ژورنال

عنوان ژورنال: Nuclear Instruments and Methods in Physics Research

سال: 2021

ISSN: ['1872-9576', '0168-9002']

DOI: https://doi.org/10.1016/j.nima.2021.165379