A5106716584- Particle Detector Development and Performance
- Particle physics theoretical and experimental studies
- Process Optimization and Integration
- Radiation Detection and Scintillator Technologies
- Machine Learning in Materials Science
- Medical Imaging Techniques and Applications
University of Padua
2025
Université Clermont Auvergne
2023
Campbell Collaboration
2023
Abstract We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering cosmic-ray muons. The exploits differentiable programming modeling muon interactions with scanned volumes, inference volume properties, optimisation cycle performing loss minimisation. In doing so, we provide first demonstration end-to-end-differentiable inference-aware particle physics instruments. study performance on relevant...
The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply measure a single physical quantity or process, but rather enable increased precision the measurement number different observable quantities natural system, extend search for new phenomena, exclude larger phase space candidate theories. Most time, combination above goals pursued; this breadth scope adds layer complexity already demanding task designing apparatus an optimal way,...
Recent advances in machine learning have opened new avenues for optimizing detector designs high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce end-to-end. AI Detector Optimization framework (AIDO), which leverages diffusion model as surrogate full simulation reconstruction chain, enabling gradient-based design exploration both continuous discrete parameter spaces. Although...
We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering cosmic-ray muons. The exploits differentiable programming modeling muon interactions with scanned volumes, inference volume properties, optimisation cycle performing loss minimisation. In doing so, we provide first demonstration end-to-end-differentiable inference-aware particle physics instruments. study performance on relevant benchmark...