Fpga · Nuclear Instruments

LEGIMaC: Low-Energy Gamma Imaging via Machine Learning in Calorimeters

LEGIMaC (Low-Energy Gamma Imaging via Machine Learning in Calorimeters) is a PNRR-funded project that developed neural network-based algorithms for waveform analysis in SiPM-coupled scintillator calorimeters. By training 1D convolutional neural networks to distinguish scintillation events from dark noise and to resolve temporal pile-up, the project pushed detection thresholds to energies previously inaccessible, and demonstrated real-time FPGA deployment via HLS4ML at power levels compatible with space missions.

SPARTAN: Space Particle Tracking with Neural Networks

SPARTAN (SPace pARTicle trAcking with Neural-networks) is a PNRR-funded project that developed an innovative machine-learning-based system for tracking charged particles in space applications. By combining Graph Neural Networks, 2D Convolutional Neural Networks, and FPGA-based real-time inference, the project demonstrated the feasibility of intelligent on-board processing for future space missions under strict power and reliability constraints.

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Everything about Fpga

LEGIMaC: Low-Energy Gamma Imaging via Machine Learning in Calorimeters

SPARTAN: Space Particle Tracking with Neural Networks