Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors

2026-05-27

AI Summary

This work addresses the long-standing challenge in scintillation detectors wherein reliance on the collective response of photodetectors impedes the resolution of individual photon arrival times, thereby limiting the attainment of picosecond-level time resolution. The authors propose a hardware-agnostic deep learning approach that integrates unsupervised learning with a physics-informed detector response model to directly estimate single-photon arrival times from raw waveforms. This method achieves, for the first time experimentally, the extraction of temporal information from individual photons in scintillators, effectively bridging the gap between theoretical modeling and experimental observation. Beyond substantially enhancing time resolution, the approach enables visualization of depth-dependent photon transport dynamics and successfully discriminates between Cherenkov and scintillation photons, establishing a data-driven paradigm for detector physics.

Abstract

Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leading to substantial improvements in diagnostic capability for diseases such as cancer and dementia. At the extreme timing precision required for such applications at the picosecond scale, detector performance is governed by the microscopic dynamics of scintillation photons generated within the detector and their subsequent detection processes. However, detector signals have conventionally been treated only as collective responses of many photons due to structural constraints inherent to photodetectors. In this study, we overcome this fundamental limitation using deep learning, enabling direct access to the timing information of individual photons. The proposed method estimates photon-by-photon arrival times directly from detector waveforms without requiring any modification to the detector structure; the method operates on an event-by-event basis without ground-truth labels by integrating an unsupervised learning framework with a physically informed detector-response model. Through comprehensive validation combining Monte Carlo simulation and experimental measurements across various detector configurations, we experimentally demonstrate improved timing resolution, visualized depth-of-interaction-dependent photon transport, and classified Cherenkov and scintillation photons based on the estimated photon-level timing information using a unified deep learning-based framework. These results provide experimental access to photon dynamics, bridging the gap between theoretical modeling and experimental observation, and they open a new data-driven pathway for discovery in detector physics and optimization.

Problem

• scintillation detectors
• photon arrival times
• timing resolution
• detector physics
• single-photon timing

Innovation

• deep learning
• photon-by-photon timing
• scintillation detectors
• unsupervised learning
• timing resolution