Related Pages
Everything about Pile-Up
Spoke 3 Technical Review Meeting in Perugia
From May 26 to 29, 2025, the periodic technical review meeting of the Spoke 3 projects was held in Perugia. Nuclear Instruments contributed with a talk on machine learning and hardware acceleration for space-based detection, presenting advances in particle track reconstruction and low-energy gamma imaging within the Spartan and LEGIMaC projects.
Read More
DT5800
Dual-channel digital detector emulator with programmable pulse generation, timing control, and noise emulation for testing radiation detection systems.
Read More
DT5810
Fast digital detector emulator with 1 ns rise time, dual-channel output, and advanced signal emulation capabilities for testing radiation detection systems.
Read More
Neural Network Pile-Up Resolution
The Super-MuSR spectrometer leverages a 1D convolutional neural network embedded in FPGA to resolve event pile-up in real time, outperforming classical deconvolution and enabling robust hit identification at gigacount rates.
Read More
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.
Read More