Orateur
Description
The ClearMind collaboration [1] aims to develop a gamma detector with a coincidence resolving time below 100 ps. The detector combines a large monolithic crystal with a photocathode deposited directly on its surface and coupled to a microchannel plate photomultiplier tube (MCP-PMT). The opposite face is instrumented with silicon photomultipliers (SiPMs) to improve depth of interaction (DOI) and timing reconstruction. We develop a deep learning approach to reconstruct the 3D interaction position, deposited energy and interaction time from the detector signals, using events simulated with Geant4.
Building on previous work on density neural networks [2], we use the full waveforms to predict a probability density for each reconstructed parameter, providing both an estimate and its associated uncertainty. A two-dimensional convolutional neural network predicts the parameters of truncated Gaussian distributions for X, Y and DOI (truncated by the crystal dimensions), and an exponentially modified Gaussian distribution for interaction time. The network was trained on 700,000 simulated events depositing 511 keV in a 60 × 60 × 5 mm³ crystal.
The DNN reconstruction method achieves FWHM resolutions of 2.1 mm, 1.3 mm and 1.7 mm for X, Y and DOI, respectively. Predicted XY position uncertainties increase within 3 mm of the lateral borders, while DOI uncertainty decreases near the MCP face and, to a lesser extent, near the SiPM face. The obtained timing resolution is 131 ps FWHM, to be compared to 222 ps and 202 ps using constant fraction discrimination (CFD) on the first detected MCP and SiPM signals, respectively, and 160 ps when combining both. Selecting, for each parameter, the 20% of events with the lowest predicted uncertainty improves the respective X, Y, DOI and timing resolutions to 1.7 mm, 1.0 mm, 0.8 mm and 100 ps. We observe that the predicted timing uncertainty is correlated to the detected charge on both detector faces, proximity to the border, and number of Cherenkovs detected.
[1] Yvon, D., V. Sharyy, M. Follin, et al. « Design study of a “scintronic” crystal targeting tens of picoseconds time resolution for gamma ray imaging: the ClearMind detector ». Journal of Instrumentation 15, nᵒ 07 (2020): P07029‑P07029. https://doi.org/10.1088/1748-0221/15/07/P07029.
[2] Daniel, G., M. B. Yahiaoui, C. Comtat, et al. « Deep learning reconstruction with uncertainty estimation for γ photon interaction in fast scintillator detectors ». Engineering Applications of Artificial Intelligence 131 (mai 2024): 107876. https://doi.org/10.1016/j.engappai.2024.107876.