Search results for "racking"

showing 10 items of 815 documents

Lifetime Measurements of Low-lying States in $^{73}$Ga and $^{70,72,74}$Zn Isotopes

2019

International audience; Lifetimes of low-lying states in 73Ga and 70,72,74Zn were measured using the Recoil Distance Doppler Shift (RDDS) method. These nuclei were produced in deep-inelastic reactions in inverse kinematics with a 208Pb beam impinging on a 76Ge target. Prompt γ rays were detected using the AGATA tracking array coupled to the VAMOS++ spectrometer. Lifetime of the 5/2 − state in 73Ga, measured for the first time, provides additional evidence for the existence of a 1/2 −, 3/2 − ground-state doublet. The lifetimes of the 4 + states in 70,72,74Zn were remeasured in an attempt to understand the discrepancies observed between earlier measurements. Our results are in agreement with …

Physicssymbols.namesakeRecoilIsotopeSpectrometersymbolsGeneral Physics and AstronomyAGATAAtomic physics[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Tracking (particle physics)Doppler effectBeam (structure)
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Performance of tracking stations of the underground cosmic-ray detector array EMMA

2018

Abstract The new cosmic-ray experiment EMMA operates at the depth of 75 m (50 GeV cutoff energy for vertical muons; 210 m.w.e.) in the Pyhasalmi mine, Finland. The underground infrastructure consists of a network of eleven stations equipped with multi-layer, position-sensitive detectors. EMMA is designed for cosmic-ray composition studies around the energy range of the knee, i.e., for primary particles with energies between 1 and 10 PeV. In order to yield significant new results EMMA must be able to record data in the full configuration for about three years. The key to the success of the experiment is the performance of its tracking stations. In this paper we describe the layout of EMMA an…

Physics::Instrumentation and DetectorsAstrophysics::High Energy Astrophysical PhenomenatutkimuslaitteetHigh-energy muonsCosmic rayScintillatorTracking (particle physics)01 natural sciencesOpticscosmic rays0103 physical sciencesAngular resolutiondrift chambersUnderground experimentCosmic rays010303 astronomy & astrophysicsImage resolutionPhysicsMuonDrift chambersta114010308 nuclear & particles physicsbusiness.industryDetectorAstronomy and Astrophysicshigh-energy muonsilmaisimetunderground experimentScintillation counterPlastic scintillation detectorsHigh Energy Physics::Experimentbusinesskosminen säteilyMuon trackingmuon trackingplastic scintillation detectorsAstroparticle Physics
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A measurement of material in the ATLAS tracker using secondary hadronic interactions in 7 TeV pp collisions

2016

Knowledge of the material in the ATLAS inner tracking detector is crucial in understanding the reconstruction of charged-particle tracks, the performance of algorithms that identify jets containing b-hadrons and is also essential to reduce background in searches for exotic particles that can decay within the inner detector volume. Interactions of primary hadrons produced in pp collisions with the material in the inner detector are used to map the location and amount of this material. The hadronic interactions of primary particles may result in secondary vertices, which in this analysis are reconstructed by an inclusive vertex-finding algorithm. Data were collected using minimum-bias trigger…

Physics::Instrumentation and DetectorsCiencias FísicasHadronsecondary [vertex]01 natural sciencesHigh Energy Physics - Experiment//purl.org/becyt/ford/1 [https]High Energy Physics - Experiment (hep-ex)[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]scattering [p p]of photons with matter interaction of hadrons with matter etc)tracking detectorInstrumentationGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)Mathematical PhysicsQCPhysicsDetector modelling and simulations I (interaction of radiation with matter interaction of photons with matter interaction of hadrons with matter etc)Performance of high energy physics detectorsLarge Hadron ColliderAtlas (topology)DetectorSettore FIS/01 - Fisica Sperimentaleexotic [particle]ATLAStrackingprimary [vertex]CERN LHC CollDetector modelling and simulations I (interaction of radiation with matter interaction of photons with matter interaction of hadrons with matter etc); Performance of High Energy Physics DetectorsAtlasTellurium compoundsParticle Physics - ExperimentperformanceCIENCIAS NATURALES Y EXACTASParticle physics530 PhysicsCiências Naturais::Ciências Físicas:Ciências Físicas [Ciências Naturais]Detector modelling and simulations I (interaction of radiation with matter interactionFOS: Physical sciencesLHC ATLAS High Energy Physics530MaterialNuclear physics510 Mathematics0103 physical sciencesddc:610High Energy Physics010306 general physicsCiencias ExactasScience & Technology010308 nuclear & particles physicstracks [charged particle]backgroundFísica//purl.org/becyt/ford/1.3 [https]triggerAstronomíaExperimental High Energy PhysicsHigh Energy Physics::Experimenthadron
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A neural network clustering algorithm for the ATLAS silicon pixel detector

2014

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. …

Physics::Instrumentation and DetectorsCiencias FísicasMonte Carlo methodHigh Energy Physics - Experiment//purl.org/becyt/ford/1 [https]High Energy Physics - Experiment (hep-ex)jetParticle tracking detectors[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]scattering [p p]Statistical physicscluster [track data analysis]Particle tracking detectors (solid-state detectors)InstrumentationQCMathematical PhysicsPhysicsArtificial neural networkAtlas (topology)Detectordetectors)Monte Carlo [numerical calculations]ATLASperformance [neural network]CERN LHC CollParticle tracking detectors (Solid-state detectors)Feature (computer vision)Physical SciencesParticle tracking detectors (Solid-stateParticle tracking detectors; Particle tracking detectors (Solid-state detectors)ComputingMethodologies_DOCUMENTANDTEXTPROCESSINGLHCConnected-component labelingAlgorithmNeural networksCIENCIAS NATURALES Y EXACTASParticle Physics - ExperimentInterpolationCiências Naturais::Ciências Físicas530 Physicssplitting:Ciências Físicas [Ciências Naturais]FOS: Physical sciencesParticle tracking detectors; Particle tracking detectors (solid-state detectors); Instrumentation; Mathematical Physics530FysikHigh Energy Physicsddc:610Cluster analysispixel [semiconductor detector]Science & TechnologyFísica//purl.org/becyt/ford/1.3 [https]High Energy Physics - Experiment; High Energy Physics - ExperimentParticle tracking detectorcluster [charged particle]AstronomíaParticle tracking detectors; Particle tracking detectors (Solid-state; detectors)Experimental High Energy Physicsimpact parameter [resolution]
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Combined performance studies for electrons at the 2004 ATLAS combined test-beam

2010

In 2004 at the ATLAS (A Toroidal LHC ApparatuS) combined test beam, one slice of the ATLAS barrel detector (including an Inner Detector set-up and the Liquid Argon calorimeter) was exposed to particles from the H8 SPS beam line at CERN. It was the first occasion to test the combined electron performance of ATLAS. This paper presents results obtained for the momentum measurement p with the Inner Detector and for the performance of the electron measurement with the LAr calorimeter (energy E linearity and resolution) in the presence of a magnetic field in the Inner Detector for momenta ranging from 20 GeV/c to 100 GeV/c. Furthermore the particle identification capabilities of the Transition Ra…

Physics::Instrumentation and DetectorsCiências Naturais::Ciências Físicas:Ciências Físicas [Ciências Naturais]Transition radiation detectorsElectronsddc:500.201 natural sciencesParticle identificationNuclear physicsCalorimetersAtlas (anatomy)Particle tracking detectors0103 physical sciencesmedicine[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental Techniques010306 general physicsNuclear ExperimentInstrumentationDetectors de radiacióMathematical PhysicsPhysicsLarge Hadron ColliderScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsDetectorCalorimetermedicine.anatomical_structureTransition radiationBeamlineHigh Energy Physics::ExperimentBeam (structure)
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Performance study of a 3×1×1 m3 dual phase liquid Argon Time Projection Chamber exposed to cosmic rays

2021

This work would not have been possible without the support of the Swiss National Science Foundation, Switzerland; CEA and CNRS/IN2P3, France; KEK and the JSPS program, Japan; Ministerio de Ciencia e Innovacion in Spain under grants FPA2016-77347-C2, SEV-2016-0588 and MdM-2015-0509, Comunidad de Madrid, the CERCA program of the Generalitat de Catalunya and the fellowship (LCF/BQ/DI18/11660043) from "La Caixa" Foundation (ID 100010434); the Programme PNCDI III, CERN-RO, under Contract 2/2020, Romania; the U.S. Department of Energy under Grant No. DE-SC0011686. This project has received funding from the European Union's Horizon 2020 Research and Innovation program under Grant Agreement no. 654…

Physics::Instrumentation and DetectorsLibrary scienceargon: gas01 natural sciences7. Clean energyNeutrino detectorArgon gasPolitical scienceParticle tracking detectors0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]media_common.cataloged_instanceNeutrino detectors[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]European union010306 general physicsInstrumentationMathematical Physicsmedia_common010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsTime projection Chambers (TPC)time projection chamber: liquid argonParticle tracking detectorTime projection chambercosmic radiationLarge detector systems for particle and astroparticle physicyield: stabilityLiquid argonperformance
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Performance study of a 3×1×1 m3 dual phase liquid Argon Time Projection Chamber exposed to cosmic rays

2021

We report the results of the analyses of the cosmic ray data collected with a 4 tonne (3×1×1 m3) active mass (volume) Liquid Argon Time-Projection Chamber (TPC) operated in a dual-phase mode. We present a detailed study of the TPC's response, its main detector parameters and performance. The results are important for the understanding and further developments of the dual-phase technology, thanks to the verification of key aspects, such as the extraction of electrons from liquid to gas and their amplification through the entire one square metre readout plain, gain stability, purity and charge sharing between readout views. peerReviewed

Physics::Instrumentation and Detectorsilmaisimettutkimuslaitteetparticle tracking detectorstime projection chambersneutriinotlarge detector systems for particle and astroparticle physicshiukkasfysiikkakosminen säteilyneutrino detectors
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Microcracking in piezoelectric materials by the Boundary Element Method

2019

A 3D boundary element model for piezoelectric polycrystalline micro-cracking is discussed in this contribution. The model is based on the boundary integral representation of the electro-mechanical behavior of individual grains and on the use of a generalized cohesive formulation for inter-granular micro-cracking. The boundary integral formulation allows to address the electro-mechanical boundary value problem in terms of generalized grain boundary and inter-granular displacements and tractions only, which implies the natural inclusion of the cohesive laws in the formulation, the simplification of the analysis pre-processing stage, and the reduction of the number of degrees of freedom of the…

Piezoelectric ceramicBoundary Element MethodPolycrystalline materialsMicrocracking
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Modelling stress-corrosion microcracking in polycrystalline materials by the Boundary Element Method

2019

The boundary element method is employed in this study in conjunction with the finite element method to build a multi-physics hybrid numerical model for the computational study of stress corrosion cracking related to hydrogen diffusion in polycrystalline microstructures. More specifically a boundary integral representation is used to represent the micro-mechanics of the aggregate while an explicit finite element method is used to model inter-granular hydrogen diffusion. The inter-granular interaction between contiguous grains is represented through cohesive laws, whose physical parameters depend on the concentration of inter-granular hydrogen, diffusing along the interfaces according to the …

Piezoelectric ceramicBoundary Element MethodPolycrystalline materialsSettore ING-IND/04 - Costruzioni E Strutture AerospazialiMicrocracking
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Meta-Tracking for Video Scene Understanding

2013

International audience; This paper presents a novel method to extract dominant motion patterns (MPs) and the main entry/exit areas from a surveillance video. The method first computes motion histograms for each pixel and then converts it into orientation distribution functions (ODFs). Given these ODFs, a novel particle meta-tracking procedure is launched which produces meta-tracks, i.e. particle trajectories. As opposed to conventional tracking which focuses on individual moving objects, meta-tracking uses particles to follow the dominant flow of the traffic. In a last step, a novel method is used to simultaneously identify the main entry/exit areas and recover the predominant MPs. The meta…

Pixelbusiness.industryComputer scienceOrientation (computer vision)Feature extractionChaotic[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineering02 engineering and technologyTracking (particle physics)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Video trackingHistogramMotion estimation0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligencebusiness
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