Speaker
Description
PHINDER, short for Picosecond-scale Photonic Heterogeneous Integrated Neuromorphic Detector, is an EIC Pathfinder Open project recently funded by the EU. PHINDER aims to develop new types of neuromorphic photonic sensor systems capable of analysing light from complex processes at the picosecond level (trillionths of a second), while consuming extremely low amounts of energy. The project combines nanostructured III–V semiconductors, programmable photonic waveguides, and neuromorphic sensor arrays into a unified hardware platform that processes time-varying signals directly on-chip.
The goal is to create an ultra-fast event camera with embedded intelligence for applications where conventional electronics fall short. Potential use cases include 5D imaging particle detectors in high-energy physics, proton computed tomography (CT) for radiation therapy, and adaptive control of chemical processes.
The application for calorimeters is based on two recent works, one on the readout system itself, and one on the hybrid particle identification.
Neuromorphic computing is based on encoding information across a "time" component: the so-encoded information can be processed in a nontrivial way with spiking neural networks. We simulate hadrons impinging on a homogeneous lead-tungstate calorimeter and detect the resulting light via an array of light-sensitive sensors whose signals we process using a neuromorphic computing system. We show that the extracted primitives offer valuable topological information about the timestamped shower development in the material, without needing to increase the granularity of the medium itself (https://doi.org/10.3390/particles8020052).
Furthermore, I will show how hadrons identification at high energies can be improved using the topology of their energy depositions in dense matter, along with the time of the interactions. We focus on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns andtiming information. Our results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers (https://doi.org/10.3390/particles8020058).