no code implementations • NeurIPS GLFrontiers Workshop 2023 • Dolores Garcia, Gregor Kržmanc, Philipp Zehetner, Jan Kieseler, Michele Selvaggi
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.
1 code implementation • 25 Sep 2023 • Giles C. Strong, Maxime Lagrange, Aitor Orio, Anna Bordignon, Florian Bury, Tommaso Dorigo, Andrea Giammanco, Mariam Heikal, Jan Kieseler, Max Lamparth, Pablo Martínez Ruíz del Árbol, Federico Nardi, Pietro Vischia, Haitham Zaraket
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.
no code implementations • 4 Apr 2022 • Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
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.
no code implementations • 20 Jan 2021 • Coralie Neubüser, Jan Kieseler, Paul Lujan
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers.
Instrumentation and Detectors
no code implementations • 24 Aug 2020 • Emil Bols, Jan Kieseler, Mauro Verzetti, Markus Stoye, Anna Stakia
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC.
no code implementations • 8 Aug 2020 • Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
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.
no code implementations • 10 Feb 2020 • Jan Kieseler
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.
3 code implementations • 21 Feb 2019 • Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama, Maurizio Pierini
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction.
1 code implementation • 6 Jun 2017 • Jan Kieseler
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