End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors
🤖 AI Summary
This work proposes the first end-to-end differentiable simulator for optical particle detectors, unifying light generation, propagation, detection, calibration, and particle trajectory reconstruction within a single differentiable framework. Traditional approaches rely on non-end-to-end pipelines that decouple calibration from reconstruction, hindering efficient and accurate mapping of sensor signals to physical quantities. By integrating physics-based modeling with automatic differentiation, the proposed method enables gradient-driven joint optimization and features a modular architecture adaptable to diverse detector geometries and materials. Experimental results demonstrate that the simulator matches or exceeds conventional methods in both accuracy and speed, while substantially streamlining the analysis workflow. These findings underscore the framework’s practicality and scalability for current and future optical particle detection experiments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
optical particle detectors
calibration
reconstruction
differentiable simulation
Cherenkov detectors
Innovation
Methods, ideas, or system contributions that make the work stand out.
differentiable simulation
end-to-end calibration
optical particle detection
gradient-based reconstruction
modular detector design
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