Publications

Les membres d'ATLAS France participent aux publications de la collaboration.

Physique et détecteurs

Software & Computing

Les résultats publics de la collaboration sur les aspects software et computing sont disponibles sur la page suivante.

2024

  • Software and computing for Run 3 of the ATLAS experiment at the LHC, ATLAS Coll., https://arxiv.org/abs/2404.06335
  • ATLAS Collaboration, Sensor Response and Radiation Damage Effects for the 3D Pixel in the ATLAS IBL Detector, ATLAS Coll., submitted to arXiv and JINST
  • The Landscape of Unfolding with Machine Learning, N. Huetsch, A. Butter (LPNHE) et al., arxiv 2404.18807
  • Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods, M. Backes, A. Butter (LPNHE), M. Dunford, B. Malaescu (LPNHE), Eur.Phys.J.C 84 (2024) 8, 770
  • Kiwaku, a C++20 library for multidimensional arrays. Application to ACTS tracking, Sylvain Joube (IJCLab, Orsay and U. Paris-Saclay), Hadrien Grasland (IJCLab, Orsay), David Chamont (IJCLab, Orsay), Joël Falcou (U. Paris-Saclay), EPJ Web Conf. 295 (2024) 03021
  • The ATLAS Event Picking Service and Its Evolution, 13 authors including G. Rybkin (IJCLAB), Phys.Part.Nucl. 55 (2024) 3, 437-440
  • A lightweight algorithm to model radiation damage effects in Monte Carlo events for High-Luminosity LHC experiments, M. Bomben (APC), K. Nakkalil (APC), Sensors 2024, 24(12), 3976

2023

  • Software performance of the ATLAS Track reconstruction, ATLAS Coll., https://arxiv.org/abs/2308.09471
  • Experimental Particle Physics and Artificial Intelligence, D. Rousseau (IJCLab), Part of Artificial Intelligence for Science: A Deep Learning Revolution, 447-464, https://inspirehep.net/literature/2673116
  • Jet Diffusion versus JetGPT -- Modern Networks for the LHC, A. Butter (LPNHE) et al., https://inspirehep.net/literature/2660878
  • Precision-Machine Learning for the Matrix Element Method, T. Heimel, A. Butter (LPNHE) et al., arxiv 2310.07752
  • pyBumpHunter: A model independent bump hunting tool in Python for high energy physics analyses, L. Vaslin, S. Calvet, V. Barra, J. Donini (LPC), doi: 10.21468/SciPostPhysCodeb.15
  • Ranking-based neural network for ambiguity resolution in ACTS, Corentin Allaire, Françoise Bouvet, Hadrien Grasland, David Rousseau, https://arxiv.org/abs/2312.05070
  • Auto-tuning capabilities of the ACTS track reconstruction suite, Allaire et al., https://arxiv.org/abs/2312.05123

2022

2021

  • A common tracking software project, H. Grasland, D. Rousseau et al. Comput.Softw.Big Sci. 6 (2022) 1, 8, https://inspirehep.net/literature/1870330
  • Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC, C. Biscarat, S. Caillou, C. Rougier, J. Stark, J. Zahreddine (L2IT), arXiv:2103.00916 [physics.ins-det]
  • The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics, G. Kasieczka et al. (LPC), arxiv:2101.08320
  • Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider, A. Stakia et al (LPC), Rev. Phys. 7 (2021) 100063
  • Resource-efficient inference for particle physics, D. Rousseau (IJCLab), Nat Mach Intell 3, 656–657 (2021), author shareable link

2020

  • ATLAS HL-LHC Computing Conceptual Design Report, CERN-LHCC-2020-015 ; LHCC-G-178
  • Towards an Understanding of Augmented Reality Extensions for Existing 3D Data Analysis Tools, Xiyao Wang, Lonni Besançon, David Rousseau, Mickael Sereno, Mehdi Ammi, Tobias Isenberg, CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems April 2020 Pages 1–13
  • On the road to a scientific data lake for the High Luminosity LHC era, S. Jézéquel (LAPP) et al., Int.J.Mod.Phys.A 35 (2020) 33, 2030022 https://inspirehep.net/literature/1835832
  • The Quest to solve the HL-LHC data access puzzle, S. Jézéquel (LAPP) et al., EPJ Web Conf. 245 (2020) 04027, https://inspirehep.net/literature/1831555
  • Implementation of ATLAS Distributed Computing monitoring dashboards using InfluxDB and Grafana, Thomas Beermann, Aleksandr Alekseev, Dario Baberis, Sabine Crépé-Renaudin, Johannes Elmsheuser, Ivan Glushkov, Michal Svatos, Armen Vartapetian, Petr Vokac and Helmut Wolters, EPJ Web of Conferences 245, 03031 (2020) (https://doi.org/10.1051/epjconf/202024503031)
  • Implementation and performances of a DPM federated storage and integration within the ATLAS environment, C. Adam, F. Chollet, Sabine Crépé-Renaudin, C. Gondrand, M. Gougerot, S. Jézéquel, P. Séraphin, EPJ Web of Conferences 245, 04045 (2020) (https://doi.org/10.1051/epjconf/202024504045)

2019

2018

  • Machine Learning in High Energy Physics Community White Paper, arxiv 1807.02876;
  • HEP Community White Paper on Software trigger and event reconstruction, arxiv 1802.08638;
  • OpTHyLiC: An Optimised Tool for Hybrid Limits Computation, Emmanuel Busato, David Calvet, Timothée Theveneaux-Pelzer, Computer Physics Communications Volume 226, May 2018, Pages 136-150, https://doi.org/10.1016/j.cpc.2018.01.009, http://opthylic.in2p3.fr/
  • Machine learning at the energy and intensity frontiers of particle physics, Alexander Radovic, Michael Williams, David Rousseau, Michael Kagan, et al. (2018), Nature, 560(7716), 41, author shareable link

2017

  • Computing shifts to monitor ATLAS distributed computing infrastructure and operations, C. Adam, D. Barberis, S. Crépé-Renaudin, K. De, F. Fassi, A. Stradling, M. Svatos, A. Vartapetian and H. Wolters, J. Phys.: Conf. Ser. 898 092004 (2017) ATL-SOFT-PROC-2017-004 (https://iopscience.iop.org/article/10.1088/1742-6596/898/9/092004)

2015

  • The Higgs boson machine learning challenge, D. Rousseau (LAL), Nature Machine Intelligence, Proceedings of the NIPS 2014 workshop on High Energy Physics and Machine Learning, novembre 2015
  • Des données brutes au boson de Higgs”,séquence du MOOC "Des particules aux étoiles", D. Rousseau (LAL), France Université Numérique, novembre 2015

2014

2012