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Found 9 papers, 3 papers with code
Towards Particle Flow Event Reconstruction at the Future Circular Collider with GNNs
Reconstructing particles properties from raw signals measured in particle physics detectors is a challenging task due to the complex shapes of the showers, variety in density and sparsity.
TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons.
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector.
Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers.
Instrumentation and Detectors
Jet Flavour Classification Using DeepJet
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC.
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals.
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction.
A method and tool for combining differential or inclusive measurements obtained with simultaneously constrained uncertainties
The best approach for a combination of these measurements would be the maximisation of a combined likelihood, for which the full fit model of each measurement and the original data are required.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Applications
