A5014427305- 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
- Cosmology and Gravitation Theories
- Distributed and Parallel Computing Systems
- Medical Imaging Techniques and Applications
- Particle Accelerators and Free-Electron Lasers
- Radiation Detection and Scintillator Technologies
- Particle accelerators and beam dynamics
- Astrophysics and Cosmic Phenomena
- Big Data Technologies and Applications
- Advanced Data Storage Technologies
- Muon and positron interactions and applications
- Generative Adversarial Networks and Image Synthesis
- Scientific Computing and Data Management
- Advanced X-ray and CT Imaging
- Hydrocarbon exploration and reservoir analysis
- Nuclear reactor physics and engineering
- advanced mathematical theories
- Black Holes and Theoretical Physics
- Atomic and Subatomic Physics Research
Max Planck Institute for Physics
2023-2025
Istituto Nazionale di Fisica Nucleare, Sezione di Roma I
2019-2024
Northern Illinois University
2023-2024
Sapienza University of Rome
2019-2024
Institute for High Energy Physics
2023-2024
Institute of Science and Technology
2023-2024
A. Alikhanyan National Laboratory
2024
Atlas Scientific (United States)
2024
Centre National de la Recherche Scientifique
2011-2023
The University of Adelaide
2013-2023
The summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements 869 papers, we list, evaluate, average measured properties gauge bosons the recently discovered Higgs boson, leptons, quarks, mesons, baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle search limits are listed in Summary Tables. give numerous tables, figures, formulae, reviews topics...
This Report summarizes the proceedings of 2015 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) PDF4LHC parton distributions, (III) issues in theoretical description production Higgs bosons and how to relate experimental measurements, (IV) a host phenomenological studies essential comparing LHC data from Run I predictions projections future measurements II, (V) Monte Carlo event generators.
This Report summarizes the proceedings of 2015 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) PDF4LHC parton distributions, (III) issues in theoretical description production Higgs bosons and how to relate experimental measurements, (IV) a host phenomenological studies essential comparing LHC data from Run I predictions projections future measurements II, (V) Monte Carlo event generators.
First-principle simulations are at the heart of high-energy physics research program. They link vast data output multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range applications modern machine learning to event generation simulation-based inference, including conceptional developments driven by specific requirements particle physics. New ideas tools developed interface will improve speed precision forward simulations, handle...
Abstract In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties particles produced within detector acceptance during collisions. At heart PFlow is ability distinguish calorimeter energy deposits neutral from those charged particles, using complementary measurements particle tracking devices, a superior measurement content kinematics. this paper, computer vision approach fundamental aspect...
Abstract The task of reconstructing particles from low-level detector response data to predict the set final state in collision events represents a set-to-set prediction requiring use multiple features and their correlations input data. We deploy three separate neural network architectures reconstruct containing single jet fully-simulated calorimeter. Performance is evaluated terms particle reconstruction quality, properties regression, jet-level metrics. results demonstrate that such...
The final results of the ALEPH search for Standart Model Higgs boson at LEP, with data collected in year 2000 center-of-mass energies up to 209 GeV, are presented. changes respect preceding publication described and a complete study systematic effects is reported. findings this analysis confirm preliminary published November shortly after closing down LEP collider: significant excess events observed, consistent production 115 GeV/c2 Standard boson. searches neutral bosons od MSSM also...
First-principle simulations are at the heart of high-energy physics research program. They link vast data output multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range applications modern machine learning to event generation simulation-based inference, including conceptional developments driven by specific requirements particle physics. New ideas tools developed interface will improve speed precision forward simulations, handle...
Abstract Multidimensional efficiency maps are commonly used in high-energy physics experiments to mitigate the limitations generation of large samples simulated events. Binned however strongly limited by statistics. We propose a neural network approach learn ratios local densities estimate an optimal fashion efficiencies as function set parameters. Graph techniques account for high dimensional correlations between different objects event. show specific toy model how this method is applicable...
Abstract A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the Geant4 toolkit and interfaced with Pythia event generator. This open-source project aimed to support development of machine learning algorithms in high energy physics that rely realistic particle shower descriptions, such as reconstruction, fast simulation, low-level analysis. Specifications granularity material its nearly hermetic geometry are user-configurable. The tool supplemented simple...
Abstract The simulation of particle physics data is a fundamental but computationally intensive ingredient for analysis at the large Hadron collider, where observational set-valued generated conditional on set incoming particles. To accelerate this task, we present novel generative model based graph neural network and slot-attention components, which exceeds performance pre-existing baselines.
The simulation of particle physics data is a fundamental but computationally intensive ingredient for analysis at the Large Hadron Collider, where observational set-valued generated conditional on set incoming particles. To accelerate this task, we present novel generative model based graph neural network and slot-attention components, which exceeds performance pre-existing baselines.
▪ Abstract The legacy of the LEP program encompasses an extensive investigation electroweak interaction and most comprehensive search to date for origin spontaneous symmetry breaking. results comprise a large variety theoretical models challenged by dedicated searches persistent standard-model Higgs boson. direct boson confronted excess signal-like events in final year. This observation reaches significance approximately two standard deviations mass 115.6 GeV/c 2 , value consistent with...
Multidimensional efficiency maps are commonly used in high energy physics experiments to mitigate the limitations generation of large samples simulated events. Binned multidimensional however strongly limited by statistics. We propose a neural network approach learn ratios local densities estimate an optimal fashion efficiencies as function set parameters. Graph techniques account for dimensional correlations between different objects event. show specific toy model how this method is...
Abstract Data preservation is a mandatory specification for any present and future experimental facility it cost-effective way of doing fundamental research by exploiting unique data sets in the light continuously increasing theoretical understanding. This document summarizes status high energy physics. The paradigms methodological advances are discussed from perspective more than ten years experience with structured effort at international level. scientific return related to accumulated...