End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors

Abstract

Large-scale homogeneous detectors with optical readouts are widely used in particle detection, with Cherenkov and scintillator neutrino detectors as prominent examples. Analyses in experimental physics rely on high-fidelity simulators to translate sensor-level information into physical quantities of interest. This task critically depends on accurate calibration, which aligns simulation behavior with real detector data, and on tracking, which infers particle properties from optical signals. We present the first end-to-end differentiable optical particle detector simulator, enabling simultaneous calibration and reconstruction through gradient-based optimization. Our approach unifies simulation, calibration, and tracking, which are traditionally treated as separate problems, within a single differentiable framework. We demonstrate that it achieves smooth and physically meaningful gradients across all key stages of light generation, propagation, and detection while maintaining computational efficiency. We show that gradient-based calibration and reconstruction greatly simplify existing analysis pipelines while matching or surpassing the performance of conventional non-differentiable methods in both accuracy and speed. Moreover, the framework's modularity allows straightforward adaptation to diverse detector geometries and target materials, providing a flexible foundation for experiment design and optimization. The results demonstrate the readiness of this technique for adoption in current and future optical detector experiments, establishing a new paradigm for simulation and reconstruction in particle physics.

Figures (17)

• Figure 1: Examples of three detector geometries built parametrically using LUCiD.
• Figure 2: An example muon event generated with PhotonSim. The particle origin is denoted with a red star symbol and the photons creation time is indicated in color. A total of 1k photons chosen at random are also represented by arrows.
• Figure 3: Three example energy slices for the surrogate model of $f^{\mathrm{Cherenkov}}$ using muons.
• Figure 4: Example of the parametrization of $f^{t_0}_\gamma$ for muons in water after subtracting $t_0 = s/c$, which accounts for the $t_0$ correction arising from the particle slowdown in the medium. Data points are shown as stars, with solid lines of matching colors representing the predictions from Eq. \ref{['eq:t0_correction']}. From darker to lighter colors (left to right), the curves correspond to muon energies ranging from 100 MeV to 2000 MeV in steps of 100 MeV.
• Figure 5: Visualization of the grid-based sensor lookup on the top cap of an SK-like detector. A single active grid cell is mapped to a specific subset of assigned sensors, allowing the intersection algorithm to ignore the remaining inactive sensors.