D. Rousseau
A5073281377- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Neutrino Physics Research
- Distributed and Parallel Computing Systems
- Cosmology and Gravitation Theories
- Radiation Detection and Scintillator Technologies
- Medical Imaging Techniques and Applications
- Astrophysics and Cosmic Phenomena
- Data Mining Algorithms and Applications
- Advanced Data Storage Technologies
- Particle Accelerators and Free-Electron Lasers
- Black Holes and Theoretical Physics
- advanced mathematical theories
- Superconducting Materials and Applications
- Muon and positron interactions and applications
- Smart Agriculture and AI
- Scientific Computing and Data Management
- Spectroscopy and Chemometric Analyses
- Plant Pathogens and Fungal Diseases
- Atomic and Subatomic Physics Research
- Horticultural and Viticultural Research
Institut National de Physique Nucléaire et de Physique des Particules
2016-2025
Centre National de la Recherche Scientifique
2016-2025
Laboratoire de Physique des 2 Infinis Irène Joliot-Curie
2019-2025
Université Paris-Saclay
2016-2025
Hôpital d'Enfants
2024
University of California, Santa Cruz
2023-2024
A. Alikhanyan National Laboratory
2024
Institut Agro Rennes-Angers
2023-2024
The University of Adelaide
2019-2023
Laboratoire Angevin de Recherche en Mathématiques
2018-2023
Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis the 1990s and 2000s, followed by explosion of event identification reconstruction 2010s. In this document we discuss promising future development areas machine a roadmap for their implementation, software hardware resource requirements, collaborative initiatives data science community, academia industry, training community science. The main objective connect...
The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated from the ATLAS Experiment at LHC corresponding to signal events with bosons decaying τ+τ– together background were made available public through website science organization Kaggle (kaggle.com). Participants attempted identify search region in a space 30 kinematic variables would maximize expected discovery significance process. One primary goals...
Abstract This paper reports on the second “Throughput” phase of Tracking Machine Learning (TrackML) challenge Codalab platform. As in first “Accuracy” phase, participants had to solve a difficult experimental problem linked tracking accurately trajectory particles as e.g. created at Large Hadron Collider (LHC): given $$O(10^5)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn>...
Detectors of High Energy Physics experiments, such as the ATLAS dectector [1] at Large Hadron Collider [2], serve cameras that take pictures particles produced in collision events. One key detector technologies used for measuring energy are calorimeters. Particles will lose their a cascade (called shower) electromagnetic and hadronic interactions with dense absorbing material. The number this showering process is subsequently measured across sampling layers calorimeter. deposition...
Research Article| July 01, 2002 A Field Survey of the 1946 Aleutian Tsunami in Far Emile A. Okal; Okal 1Department Geological Sciences, Northwestern University Search for other works by this author on: GSW Google Scholar Costas E. Synolakis; Synolakis 2Department Civil Engineering, Southern California Gerard J. Fryer; Fryer 3Hawaii Institute Geophysics Philippe Heinrich; Heinrich 4Département Analyse et Surveillance de l'Environnement, Commissariat à l'Énergie Atomique, France José C....
Machine learning has been applied to several problems in particle physics research, beginning with applications high-level analysis the 1990s and 2000s, followed by an explosion of event identification reconstruction 2010s. In this document we discuss promising future research development areas for machine physics. We detail a roadmap their implementation, software hardware resource requirements, collaborative initiatives data science community, academia industry, training community science....
This study aimed to set a computer-integrated multichannel spectral imaging system as high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations moisture, protein sugars during twelve stages seed development from 10 32 days after anthesis were non-destructively monitored. Multispectral data 20 discrete wavelengths ultraviolet, visible near infrared regions extracted then modelled...
The triple gauge-boson couplings, αWΦ, αW and αBΦ, have been measured using 34 semileptonically 54 hadronically decaying W+W− candidate events. events were selected in the data recorded during 1996 with ALEPH detector at 172 GeV, corresponding to an integrated luminosity of 10.65 pb−1. couplings optimal observables constructed from kinematic information results are agreement Standard Model expectation.
How to approach common bile-duct (CBD) stones discovered during laparoscopic cholecystectomy (LC) is still a subject for debate. After sequential strategies, the natural trend now towards single-stage therapy. The aim of this study was establish feasibility intraoperative endoscopic sphincterotomy (IOES) when CBD are or strongly suspected on cholangiography (IOC) LC.Out total 2193 cholecystectomies, we reviewed 57 patients who, between 1991 and 1999, underwent IOES just after LC same...
The reconstruction of the trajectories charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While tuning algorithms can depend strongly on details detector geometry, currently in use by experiments share many common features. At same time, intense environment High-Luminosity LHC accelerator other future expected to put even greater stress software, motivating development more performant algorithms. We present here A Common...