Speaker
Description
High-resolution neutrino detectors typically rely on fine detector segmentation to obtain detailed spatial information, which introduces substantial complexity, channel count, and cost. In this talk, I will present the deep-learning reconstruction developed for PLATON, a detector concept based on plenoptic imaging of scintillation light in an unsegmented scintillator volume. The central challenge is to infer the three-dimensional topology of charged-particle tracks from extremely sparse, photon-starved SPAD-array images collected by multiple plenoptic cameras. Using simulated neutrino interactions validated on prototype data, we show that deep learning can reconstruct detailed 3D event information from these optical measurements. The network predicts the 3D origins of detected scintillation photons, producing a point cloud that is subsequently used to reconstruct particle tracks, vertices, and final-state topologies. In simulated charged-current neutrino interactions, this approach achieves sub-millimetre spatial reconstruction, with resolutions down to the few-hundred-micrometre scale, enabling efficient reconstruction of multi-proton final states relevant to neutrino–nucleus interaction modelling. I will also discuss ongoing studies of the scalability of this approach to larger detector volumes and of what the neural network learns about the underlying optics, suggesting a path towards scalable, high-resolution event reconstruction in monolithic scintillator detectors.
| Contribution types | Long talk (30min + 10min Q/A) |
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