Loading...
Bienvenue sur l'archive ouverte HAL de l'Institut Polytechnique de Paris
Elle comprend les publications des 5 établissements membres de l'IP Paris (cliquer sur leur logo pour accéder à leur portail HAL) :
École polytechnique | ENSTA Paris | ENSAE Paris | Télécom Paris | Télécom SudParis |
Derniers dépôts
Documents avec texte intégral
10 823
Publications en Open Access
78 %
Références bibliographiques
7 093
Nuage de mots-clés
P p colliding beams
Statistical
CMS
Branching ratio
Optimization
Data analysis method
Programming
Transverse momentum missing-energy
Error statistical
Final state njet lepton
Channel cross section upper limit
Z0 leptonic decay
Internet of Things
Anomaly detection
Monte Carlo
Gravitation
13000 GeV-cms
Neural network
Modeling
Rapidity
Phase space
Lead
Lepton
Experimental results
Simulation
Hadron-Hadron scattering experiments
Plasma
Muon
Hadron-Hadron Scattering
Deep learning
Quark
B physics
Electroweak interaction
Neural networks
LHC-B
Parton distribution function
Rapidity dependence
Mass dependence
Climate change
Apprentissage automatique
Performance
GeV
Jet bottom
Sensitivity
Field theory conformal
Security
Resonance production
J-PARC Lab
Supersymmetry
Asymmetry CP
Correlation function
KAMIOKANDE
Optimisation
CERN Lab
Stability
Electron
Suppression
CERN LHC Coll
Bottom particle identification
Branching ratio ratio measured
Transverse momentum dependence
Machine learning
Blockchain
Kinematics
Mars
Laser
Silicon
Machine Learning
IoT
Top pair production
Channel cross section measured
TeV
Statistical analysis
New physics search for
Photon
Reinforcement learning
7000 GeV-cms
Background
P p scattering
Quantum chromodynamics
CP violation
Apprentissage profond
Differential cross section measured
Signature
Heavy ion scattering
Structure
Effective field theory
Muon pair production
Artificial intelligence
5G
Correlation
Numerical calculations Monte Carlo
Turbulence
Transverse momentum
Calibration
Final state njet dilepton
8000 GeV-cms
Privacy
Channel cross section branching ratio upper limit
Deep Learning