Position-Sensitive Silicon Photomultiplier Array with Enhanced Position Reconstruction by means of a Deep Neural Network
Abstract
Single-photon sensitive detectors like Silicon Photomultipliers are widely used in many medical imaging applications. By using detectors with position resolutions, it is possible to build compact photodetector readouts with reduced number of channels, but still preserving position resolution and gamma-rays imaging capabilities. In this work, we present the advantage of using a Deep Neural Networks (DNNs) light position reconstruction applied to a 2x2 array of linearly-graded SiPMs (LG-SiPMs), to minimize the distortions on the reconstructed event maps. Our approach significantly enhances both the resolution and linearity of position detection compared to the nominal reconstruction formula based on the device architecture. Remarkably, the DNN-based reconstruction boosts the number of resolved areas (pixels) by a factor of at least 5.7, allowing a higher level of precision and performance in light detection.
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Alispach, C., Acerbi, F., Arabi, H., Volpe, D. d., Gola, A., Raiola, A., Zaidi, H. (2025). Position-Sensitive Silicon Photomultiplier Array with Enhanced Position Reconstruction by means of a Deep Neural Network. arXiv preprint arXiv:2512.02771.
Cyril Alispach, Fabio Acerbi, Hossein Arabi, Domenico della Volpe, Alberto Gola, Aramis Raiola, and Habib Zaidi. "Position-Sensitive Silicon Photomultiplier Array with Enhanced Position Reconstruction by means of a Deep Neural Network." arXiv preprint arXiv:2512.02771 (2025).