Search results for "cluster finding"

showing 10 items of 14 documents

Deep-learning based reconstruction of the shower maximum X max using the water-Cherenkov detectors of the Pierre Auger Observatory

2021

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the groun…

showers: energylongitudinal [showers]interaction: modelPhysics::Instrumentation and DetectorsAstronomyCalibration and fitting methods; Cluster finding; Data analysis; Large detector systems for particle and astroparticle physics; Particle identification methods; Pattern recognition01 natural sciencesHigh Energy Physics - ExperimentAugerHigh Energy Physics - Experiment (hep-ex)Particle identification methodscluster findingsurface [detector]ObservatoryLarge detector systemsInstrumentationMathematical PhysicsHigh Energy Astrophysical Phenomena (astro-ph.HE)astro-ph.HEPhysicsPattern recognition cluster finding calibration and fitting methodsPhysicsSettore FIS/01 - Fisica Sperimentalemodel [interaction]DetectorAstrophysics::Instrumentation and Methods for AstrophysicsData analysicalibration and fitting methodsenergy [showers]AugerobservatoryPattern recognition cluster finding calibration and fitting methodastroparticle physicsAstrophysics - Instrumentation and Methods for AstrophysicsAstrophysics - High Energy Astrophysical Phenomenaatmosphere [showers]airneural networkAstrophysics::High Energy Astrophysical PhenomenaUHE [cosmic radiation]Data analysisFOS: Physical sciences610Cosmic raydetector: fluorescencePattern recognition0103 physical sciencesddc:530High Energy Physicsddc:610[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]cosmic radiation: UHEstructureparticle physicsnetwork: performance010306 general physicsInstrumentation and Methods for Astrophysics (astro-ph.IM)Ciencias ExactasCherenkov radiationfluorescence [detector]Pierre Auger ObservatoryCalibration and fitting methodsmass spectrum [nucleus]showers: atmospheredetector: surfacehep-ex010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsCluster findingFísicaresolutioncalibrationComputational physicsperformance [network]Cherenkov counterAir showerLarge detector systems for particle and astroparticle physicExperimental High Energy PhysicsHigh Energy Physics::Experimentnucleus: mass spectrumshowers: longitudinalRAIOS CÓSMICOSEnergy (signal processing)astro-ph.IM
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Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run

2013

This paper presents a summary of beam-induced backgrounds observed in the ATLAS detector and discusses methods to tag and remove background contaminated events in data. Triggerrate based monitoring of beam-related backgrounds is presented. The correlations of backgrounds with machine conditions, such as residual pressure in the beam-pipe, are discussed. Results from dedicated beam-background simulations are shown, and their qualitative agreement with data is evaluated. Data taken during the passage of unpaired, i.e. non-colliding, proton bunches is used to obtain background-enriched data samples. These are used to identify characteristic features of beam-induced backgrounds, which then are …

Physics::Instrumentation and DetectorsAccelerator modelling and simulations; multi-particle dynamics; Analysis and statistical methods; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; single-particle dynamicsPROTON BEAMSMonte Carlo methodsingle-particle dynamics01 natural sciencesaccelerator modelling and simulations (multi-particle dynamics; single-particle dynamics)High Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)MUON DETECTORcluster findingPIXEL DETECTORSNaturvetenskap[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]GeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)InstrumentationQCMathematical PhysicsPhysicsLarge Hadron ColliderPattern recognition cluster finding calibration and fitting methodsAccelerator modelling and simulations (multi-particle dynamics; single-particle dynamics)Settore FIS/01 - Fisica SperimentaleObservableATLAScalibration and fitting methodsAccelerator modelling and simulationsCalorimetermedicine.anatomical_structureBunchesAccelerator Modelling and Simulations (Multi-Particle Dynamics Single-Particle Dynamics)Analysis and statistical methodsLHCmulti-particle dynamicsNatural SciencesParticle Physics - ExperimentParticle physicsCiências Naturais::Ciências Físicas530 PhysicsInstrumentationCALORIMETERS:Ciências Físicas [Ciências Naturais]FOS: Physical sciencesddc:500.2530Nuclear physicssingle-particle dynamics)Atlas (anatomy)Pattern recognition0103 physical sciencesmedicineAccelerator modelling and simulations (multi-particle dynamics single-particle dynamics)High Energy Physicspattern recognition; cluster finding; calibration and fitting methods; single-particle dynamics); analysis and statistical methods; accelerator modelling and simulations (multi-particle dynamics; performance of high energy physics detectorsddc:610010306 general physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsCluster findingFísicaAccelerator modelling and simulations (multi-particle dynamicsAccelerator modelling and simulations (multi-particle dynamics; Analysis and statistical methods; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; single-particle dynamics); Instrumentation; Mathematical PhysicsExperimental High Energy PhysicsPattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors; Accelerator modelling and simulations (multi-particle dynamics; single-particle dynamics); Analysis and statistical methodsPhysics::Accelerator PhysicsPerformance of High Energy Physics DetectorsEvent (particle physics)
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A layer correlation technique for pion energy calibration at the 2004 ATLAS Combined Beam Test

2010

A new method for calibrating the hadron response of a segmented calorimeter is developed and successfully applied to beam test data. It is based on a principal component analysis of energy deposits in the calorimeter layers, exploiting longitudinal shower development information to improve the measured energy resolution. Corrections for invisible hadronic energy and energy lost in dead material in front of and between the calorimeters of the ATLAS experiment were calculated with simulated Geant4 Monte Carlo events and used to reconstruct the energy of pions impinging on the calorimeters during the 2004 Barrel Combined Beam Test at the CERN H8 area. For pion beams with energies between 20GeV…

Physics - Instrumentation and DetectorsCiências Naturais::Ciências FísicasPhysics::Instrumentation and Detectors:Ciências Físicas [Ciências Naturais]Monte Carlo methodFOS: Physical sciencesddc:500.201 natural sciences7. Clean energyPartícules (Física nuclear)Settore FIS/04 - Fisica Nucleare e SubnucleareHigh Energy Physics - ExperimentNuclear physicsCalorimetersHigh Energy Physics - Experiment (hep-ex)PionAtlas (anatomy)calorimeter methods ; pattern recognition ; cluster finding ; calibration and fitting methods ; calorimeters ; detector modelling and simulations0103 physical sciencesCalibrationmedicine[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex][PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Calorimeter methods010306 general physicsNuclear ExperimentInstrumentationMathematical PhysicsPhysicsDetector modelling and simulations I (interaction of radiation with matter interaction of photons with matter interaction of hadrons with matter etc)Science & TechnologyLarge Hadron Collider010308 nuclear & particles physicsPattern recognition cluster finding calibration and fitting methodsSettore FIS/01 - Fisica SperimentaleATLAS experimentInstrumentation and Detectors (physics.ins-det)Calorimetermedicine.anatomical_structureExperimental High Energy PhysicsComputingMethodologies_DOCUMENTANDTEXTPROCESSINGFísica nuclearHigh Energy Physics::ExperimentBeam (structure)Journal of Instrumentation
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Precision luminosity measurements at LHCb

2014

Measuring cross-sections at the LHC requires the luminosity to be determined accurately at each centre-of-mass energy $\sqrt{s}$. In this paper results are reported from the luminosity calibrations carried out at the LHC interaction point 8 with the LHCb detector for $\sqrt{s}$ = 2.76, 7 and 8 TeV (proton-proton collisions) and for $\sqrt{s_{NN}}$ = 5 TeV (proton-lead collisions). Both the "van der Meer scan" and "beam-gas imaging" luminosity calibration methods were employed. It is observed that the beam density profile cannot always be described by a function that is factorizable in the two transverse coordinates. The introduction of a two-dimensional description of the beams improves sig…

Instrumentation for particle accelerators and storage rings - high energy (linear acceleratorsHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)cluster finding[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Experiment06.20.fbInstrumentationMathematical PhysicsQCPhysicsLuminosity (scattering theory)Large Hadron ColliderPattern recognition cluster finding calibration and fitting methodssynchrotrons)DetectorPattern recognition cluster finding calibration and fitting methodsComputer interfacecalibration and fitting methodsFísica nuclearTracking and position-sensitive detectorLHCParticle Physics - ExperimentParticle physics29.40.GxPattern recognition cluster finding calibration and fitting methods; Instrumentation for particle accelerators and storage rings - high energy (linear accelerators synchrotrons)Astrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesAstrophysics::Cosmology and Extragalactic AstrophysicsLHCb - Abteilung HofmannPattern recognition cluster finding calibration and fitting methodInstrumentation for particle accelerators and storage rings - high energy (linear accelerators synchrotrons)NOConsistency (statistics)Pattern recognitionCalibrationSDG 7 - Affordable and Clean EnergyInstrumentation for particle accelerators and storage rings - high energy (linear accelerators synchrotrons)/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energyInteraction pointStandards and calibrationFunction (mathematics)29.50.+vLHCbInstrumentation for particle accelerators and storage rings - high energy (linear accelerators synchrotrons); Pattern recognition cluster finding calibration and fitting methods; Instrumentation; Mathematical PhysicsTEVPhysics::Accelerator PhysicsHigh Energy Physics::ExperimentInstrumentation for particle accelerators and storage rings - high energy (linear accelerators synchrotrons); Pattern recognition cluster finding calibration and fitting methodsEnergy (signal processing)
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Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory

2021

The successful installation, commissioning, and operation of the Pierre Auger Observatory would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargue. We are very grateful to the following agencies and organizations for financial support: Argentina -Comision Nacional de Energia Atomica; Agencia Nacional de Promocion Cientifica y Tecnologica (ANPCyT); Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Gobierno de la Provincia de Mendoza; Municipalidad de Malargue; NDM Holdings and Valle Las Lenas; in gratitude for their continuing cooperation over land access; Australia -the Australian Research Council; Braz…

Physics - Instrumentation and DetectorsPhysics::Instrumentation and DetectorsAstronomyPerformance of High Energy Physics Detector01 natural sciences7. Clean energyEtc)030218 nuclear medicine & medical imaging0302 clinical medicineFront-end electronics for detector readoutAPDsInstrumentationphysics.ins-detPhoton detectors for UVMathematical PhysicsInstrumentation et méthodes en physiqueEBCCDsVisible and IR photons (solid-state) (PIN diodes APDs Si-PMTs G-APDs CCDs EBCCDs EMCCDs CMOS imagers etc)electronicsSettore FIS/01 - Fisica SperimentaleCalibration and fitting methods; Performance of High Energy Physics Detectors; Photon detectors for UVPhoton detectors for UV visible and IR photons (solid-state) (PIN diodes APDs Si-PMTs G-APDs CCDs EBCCDs EMCCDs CMOS imagers etc)Astrophysics::Instrumentation and Methods for AstrophysicsSi-PMTsInstrumentation and Detectors (physics.ins-det)charged particleAPDs; Calibration and fitting methods; Performance of High Energy Physics Detectors; Photon detectors for UV; CCDs; Cluster finding; CMOS imagers; EBCCDs; EMCCDs; Etc); Front-end electronics for detector readout; Pattern recognition; G-APDs; Si-PMTs; Visible and IR photons (solid-state) (PIN diodesAugerobservatorydensity [muon]Pattern recognition cluster finding calibration and fitting methodG-APDsChristian ministryupgradeddc:620Astrophysics - Instrumentation and Methods for Astrophysicsperformanceatmosphere [showers]Land accessCherenkov counter: waterairAstrophysics::High Energy Astrophysical PhenomenaUHE [cosmic radiation]FOS: Physical sciencesVisible and IR photons (solid-state) (PIN diodes03 medical and health sciencesPolitical sciencePattern recognition0103 physical sciencesmuon: densityFront-end electronics for detector readout; Pattern recognitionphotomultiplier: siliconHigh Energy Physicscosmic radiation: UHE[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]ddc:610CMOS imagersInstrumentation and Methods for Astrophysics (astro-ph.IM)Engineering & allied operationsscintillation counterCalibration and fitting methodsshowers: atmosphere010308 nuclear & particles physicswater [Cherenkov counter]Cluster findingAutres mathématiquesCCDsEMCCDsResearch councilefficiencyExperimental High Energy Physicssilicon [photomultiplier]Performance of High Energy Physics DetectorsHigh Energy Physics::ExperimentHumanitiesRAIOS CÓSMICOSastro-ph.IM
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FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions

2019

The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions sinc…

Calibration and fitting methods010308 nuclear & particles physicsSignal reconstructionComputer scienceCluster findingDetectorTime signal01 natural sciencesSignal030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineSignal-to-noise ratioAnalog signalPattern recognitionData processing methods0103 physical sciencesSimulation methods and programsInstrumentationDigital filterAlgorithmMathematical PhysicsEnergy (signal processing)Journal of Instrumentation
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Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array

2013

NEXT-DEMO is a high-pressure xenon gas TPC which acts as a technological test-bed and demonstrator for the NEXT-100 neutrinoless double beta decay experiment. In its current configuration the apparatus fully implements the NEXT-100 design concept. This is an asymmetric TPC, with an energy plane made of photomultipliers and a tracking plane made of silicon photomultipliers (SiPM) coated with TPB. The detector in this new configuration has been used to reconstruct the characteristic signature of electrons in dense gas, demonstrating the ability to identify the MIP and "blob" regions. Moreover, the SiPM tracking plane allows for the definition of a large fiducial region in which an excellent e…

PhotomultiplierMECANICA DE LOS MEDIOS CONTINUOS Y TEORIA DE ESTRUCTURASPhysics - Instrumentation and DetectorsPhysical measurementsParticle tracking detectors (Gaseous detectors)Time projection chambersPattern recognition SystemsFísica -- Mesuramentschemistry.chemical_elementFOS: Physical sciencesTracking (particle physics)01 natural sciences7. Clean energyTECNOLOGIA ELECTRONICAXenonSilicon photomultiplierOpticsCluster analysisDouble beta decayPattern recognition0103 physical sciencesCalibrationReconeixement de formes (Informàtica)Calibratge010306 general physicsInstrumentationImage resolutionMathematical PhysicsDetectors de radiacióPhysicsCalibration and fitting methods010308 nuclear & particles physicsbusiness.industryDetectorCluster findingFísicaInstrumentation and Detectors (physics.ins-det)Double-beta decay detectorsAnàlisi de conglomeratschemistryNuclear countersCalibrationbusiness
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Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment

2013

NEXT-DEMO is a large-scale prototype of the NEXT-100 detector, an electroluminescent time projection chamber that will search for the neutrinoless double beta decay of Xe-136 using 100-150 kg of enriched xenon gas. NEXT-DEMO was built to prove the expected performance of NEXT-100, namely, energy resolution better than 1% FWHM at 2.5MeV and event topological reconstruction. In this paper we describe the prototype and its initial results. A resolution of 1.75% FWHM at 511 keV (which extrapolates to 0.8% FWHM at 2.5 MeV) was obtained at 10 bar pressure using a gamma-ray calibration source. Also, a basic study of the event topology along the longitudinal coordinate is presented, proving that it…

MECANICA DE LOS MEDIOS CONTINUOS Y TEORIA DE ESTRUCTURASPhysics - Instrumentation and DetectorsBar (music)Isòtops radioactius -- DesintegracióTime projection chambersPattern recognition SystemsFOS: Physical scienceschemistry.chemical_elementElectron7. Clean energy01 natural sciencesNuclear physicsTECNOLOGIA ELECTRONICAXenonCambres d'ionitzacióCluster analysisDouble beta decayPattern recognition0103 physical sciencesCalibrationReconeixement de formes (Informàtica)Calibratge010306 general physicsInstrumentationMathematical PhysicsRadioisotopes -- DecayPhysicsCalibration and fitting methodsTime projection chamber010308 nuclear & particles physicsDetectorCluster findingFísicaInstrumentation and Detectors (physics.ins-det)Double-beta decay detectorsAnàlisi de conglomeratschemistryCalibrationEvent (particle physics)Ionization Chambers
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The ALICE experiment at the CERN LHC

2008

Journal of Instrumentation 3(08), S08002 (2008). doi:10.1088/1748-0221/3/08/S08002

visible and IR photonsLiquid detectorshigh energyPhotonPhysics::Instrumentation and DetectorsTransition radiation detectorsTiming detectors01 natural sciencesOverall mechanics designParticle identificationSoftware architecturesParticle identification methodsGaseous detectorscluster findingDetector cooling and thermo-stabilizationDetector groundingParticle tracking detectors[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Special cablesDetector alignment and calibration methodsDetectors and Experimental TechniquesNuclear ExperimentVoltage distributions.Photon detectors for UVInstrumentationMathematical PhysicsQuantum chromodynamicsPhysicsLarge Hadron ColliderSpectrometersPhysicsDetectorcalibration and fitting methodsTransition radiation detectorScintillatorsData processing methodsAnalysis and statistical methodsData reduction methodsParticle physicsCherenkov and transition radiationTime projection chambers610dE/dx detectorsNuclear physicsCalorimetersPattern recognitionGamma detectors0103 physical sciencesddc:610Solid state detectors010306 general physicsMuonInstrumentation for heavy-ion acceleratorsSpectrometerLarge detector systems for particle and astroparticle physics010308 nuclear & particles physicsCERN; LHC; ALICE; heavy ion; QGPCherenkov detectorsComputingVoltage distributionsManufacturingscintillation and light emission processesanalysis and statistical methods; calorimeters; cherenkov and transition radiation; cherenkov detectors; computing; data processing methods; data reduction methods; de/dx detectors; detector alignment and calibration methods; detector cooling and thermo-stabilization; detector design and construction technologies and materials; detector grounding; gamma detectors; gaseous detectors; instrumentation for heavy-ion accelerators; instrumentation for particle accelerators and storage rings - high energy; large detector systems for particle and astroparticle physics; liquid detectors; manufacturing; overall mechanics design; particle identification methods; particle tracking detectors; pattern recognition; cluster finding; calibration and fitting methods; photon detectors for uv; visible and ir photons; scintillators; scintillation and light emission processes; simulation methods and programs; software architectures; solid state detectors; special cables; spectrometers; time projection chambers; timing detectors; transition radiation detectors; voltage distributionsInstrumentation for particle accelerators and storage ringsInstrumentation; Mathematical PhysicsHigh Energy Physics::ExperimentSimulation methods and programsDetector design and construction technologies and materials
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A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

2021

Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction …

FOS: Computer and information sciencesComputer Science - Machine LearningAstrophysics::High Energy Astrophysical Phenomenacs.LGData analysisFOS: Physical sciencesFitting methods01 natural sciencesConvolutional neural networkCalibration; Cluster finding; Data analysis; Fitting methods; Neutrino detectors; Pattern recognitionHigh Energy Physics - ExperimentIceCube Neutrino ObservatoryMachine Learning (cs.LG)High Energy Physics - Experiment (hep-ex)Pattern recognition0103 physical sciencesNeutrino detectors010303 astronomy & astrophysicsInstrumentationMathematical Physics010308 nuclear & particles physicsbusiness.industryhep-exDeep learningCluster findingDetectorNeutrino detectorComputer engineeringOrders of magnitude (time)13. Climate actionCascadeCalibrationPattern recognition (psychology)Artificial intelligencebusiness
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