Speaker
Description
Accurate photon timing in large liquid-scintillator neutrino detectors requires precise modeling of complex optical processes. These include complex scattering, absorption and re-emission, boundary effects, steel-frame shadowing, and multi-path reverberation. These effects draw a complex picture that is yet intractable for analytic methods and too detector-specific to learn end-to-end. We present a hybrid physics--ML architecture, RTE--cNSF, designed for the JUNO detector, which combines a physics-based radiative transfer equation (RTE) solver with a conditional neural spectral field (cNSF) to model these processes.
The architecture enforces a strict first-hit handoff boundary: the RTE handles light propagation inside the liquid scintillator up to the acrylic boundary, while a frozen cNSF --- trained on filtered GEANT4 tracks --- captures all subsequent water-buffer physics. Exploiting the SO(2) symmetry of individual PMTs, the neural model operates on a compact, three-parameter local invariant space, preventing the leakage of source-specific information into the boundary model.
The spatial integration is trifurcated into three topologically disjoint pipelines: a target-driven Fermat path solved analytically via 1D auto-differentiation (JAX) with a symmetry-reduced Jacobian; a diffuse direct-light channel sampled over a 2D Sobol sequence with an orthogonality veto mask; and a scattered-light channel over a 5D boundary manifold. The resulting optical delay kernel is assembled by convolving the spatially integrated flux with the cNSF timing distribution, the scintillator emission profile, chromatic dispersion, and the PMT transit-time spread via FFT, preserving full differentiability for parameter inference.
This work aims to provide a computationally efficient and physically accurate model for photon timing in the JUNO detector, enabling improved calibration, event reconstruction, and ultimately enhancing the sensitivity of neutrino measurements.
| Contribution types | Standard talk (20min + 5min Q/A) |
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