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Thesis
Home   /   Post Doctorat   /   Signal processing of ultra-fast gamma-ray detectors using Machine Learning

Signal processing of ultra-fast gamma-ray detectors using Machine Learning

Engineering sciences Instrumentation Mathematics - Numerical analysis - Simulation

Abstract

In the frame of the ANR project AAIMME dedicated to the Positron-Emission Tomography (PET), we propose a 24-month post-doctoral position that will focus on the development of signal processing methods for the detector ClearMind, designed at the CEA-IRFU. The detector is specifically developed to provide a precise interaction time in the sensitive volume. It consists of a scintillator PbWO4 detector, coupling with a Micro Channel Plate PhotoMultiplier Tube, whose signals are digitized using fast acquisition modules SAMPIC. The main advantage is to exploit both fast Tcherenkov and scintillation photons to reconstruct as accurately as possible the interactions inside the Crystal.
The analysis of the detector signal represents a major challenge: they are complex and intricated, thus, it necessitates a dedicated processing step.
The objective of this post-doc is to develop these trustworthy Machine Learning algorithms to reconstruct the properties of the gamma-ray interaction in the detector, with the highest achievable accuracy, using the detector signals.

Laboratory

Département de Modélisation des Systèmes et Structures
SERVICE DE GENIE LOGICIEL POUR LA SIMULATION
Laboratoire d'Intelligence Artificielle et de science des Données
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