AI-Powered Camera Reconstructs Particle Paths Using Just a Handful of Photons
Researchers at ETH Zurich and EPFL have developed a new particle detection system that uses artificial intelligence to reconstruct particle trajectories from only a handful of detected photons. The technology, known as PLATON, could simplify the design of particle detectors while delivering performance comparable to today’s most advanced systems.
The researchers believe the innovation could also improve medical imaging technologies, including positron emission tomography (PET) scanners, by reconstructing faint light signals with greater accuracy.
A Simpler Approach to Particle Detection
Particle detectors are essential tools in physics experiments, allowing scientists to observe the paths of elementary particles as they pass through specialised materials.
Traditional detectors use scintillators, which emit tiny flashes of light when struck by charged particles. To determine where these interactions occur, the scintillator is typically divided into millions of small segments connected to optical fibres and photon sensors.
Although this design provides high precision, it becomes increasingly complex and costly as detectors grow larger. Major experiments, including Japan’s T2K neutrino experiment and CERN’s LHCb, rely on millions of individual detector components to achieve high spatial resolution.
PLATON takes a different approach by replacing those segmented structures with a single block of scintillating material. Instead of relying on numerous sensors, the system reconstructs particle interactions using advanced imaging and artificial intelligence.
AI and Light-Field Imaging Work Together
At the heart of PLATON is a plenoptic, or light-field, camera capable of recording both the intensity of incoming light and the direction from which it travels.
The system combines this camera with a single-photon avalanche diode (SPAD) sensor, enabling it to detect individual photons produced inside the scintillator.
Researchers built the prototype using a micro-lens array mounted directly onto the SwissSPAD2 imaging sensor. The design captures extremely faint flashes of light while reducing background noise through gated photon detection.
Laboratory tests showed that the detector could successfully reconstruct particle interactions using as few as five detected photons. Simulations closely matched the experimental findings, confirming the system’s performance.
Transformer AI Reconstructs Particle Paths
The research team also developed a neural network based on Transformer architecture, the same family of artificial intelligence models widely used in modern large language models.
Rather than processing text, the AI analyses the location and timing of detected photons to determine where the original particle interaction occurred.
According to simulations, a future version of PLATON could achieve sub-millimetre spatial resolution within a detector measuring 10 × 10 × 10 centimetres while effectively identifying neutrino interactions.
The researchers also modelled a much larger detector measuring one cubic metre. Even at that scale, simulations indicated that the system could maintain spatial resolution of only a few millimetres, matching the performance of many existing scintillator detectors despite using far fewer components.
Potential Benefits for Medical Imaging
Beyond particle physics, the researchers believe PLATON could have important applications in healthcare.
The team has already filed three patents covering the use of the technology in positron emission tomography (PET) scanners. By reconstructing extremely faint light signals more accurately, the system could improve image quality and produce sharper medical scans.
If successfully developed for clinical use, the technology could become another example of advances in particle physics contributing to innovations in medical imaging and other scientific fields.
The research findings were recently published in Nature Communications.