Article-Journal

Machine Learning Approach to Shield Optimization at Muon Collider
Muon collisions are considered a promising means for exploring the energy frontier, leading to a detailed study of the possible …
Cite DOI URL
Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications
The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing …
Cite DOI URL
Neuromorphic Readout for Hadron Calorimeters
We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4) calorimeter using GEANT4 software to investigate how the …
Cite DOI URL
Optimisation of Muon Tomography Scanners for Border Control Using TomOpt
The TomOpt software package is designed to optimise the geometric configuration and the specifications of detectors intended for muon …
Cite DOI URL
Optimization pipeline for in-ice radio neutrino detectors
Cite DOI
Porting MADGRAPH to FPGA Using High-Level Synthesis (HLS)
The escalating demand for data processing in particle physics research has spurred the exploration of novel technologies to enhance the …
Cite DOI URL
Production Optimization of Exotic Hypernuclei via Heavy-Ion Beams at GSI-FAIR
Building on the successful demonstration of hypernuclear spectroscopy using heavy-ion beams, the HypHI Collaboration is shifting its …
Cite DOI URL
Scattering-Based Machine Learning Algorithms for Momentum Estimation in Muon Tomography
Muon tomography leverages the small, continuous flux of cosmic rays produced in the upper atmosphere to measure the density of unknown …
Cite DOI URL
Unsupervised Particle Tracking with Neuromorphic Computing
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of …
Cite DOI URL
Versal Adaptive Compute Acceleration Platform Processing for ATLAS-TileCal Signal Reconstruction
Particle detectors at accelerators generate large amounts of data, requiring analysis to derive insights. Collisions lead to signal …
Cite DOI URL