Search results for "Fitting method"

showing 10 items of 21 documents

“RecPack” a reconstruction toolkit

2004

We present a C++ toolkit to do tracking and vertex reconstruction. The toolkit incorporates common fitting methods, as the Kalman Filter, a framework to define a detector setup, a general navigation and a simple simulation. Furthermore, the toolkit provides a collection of interfaces which facilitates the addition of new fitting methods, trajectory models, geometrical objects, pattern recognition logic, etc. Although the toolkit was originally developed to be used in High Energy Physics, it could be applied to other fields.

PhysicsNuclear and High Energy PhysicsVertex (computer graphics)Fitting methodsSimple (abstract algebra)DetectorPattern recognition (psychology)TrajectoryKalman filterTracking (particle physics)InstrumentationComputational scienceNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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Interpretation of Ocular Melanin Drug Binding Assays. Alternatives to the Model of Multiple Classes of Independent Sites

2016

Melanin has a high binding affinity for a wide range of drugs. The determination of the melanin binding capacity and its binding affinity are important, e.g., in the determination of the ocular drug distribution, the prediction of drug effects in the eye, and the trans-scleral drug delivery. The binding parameters estimated from a given data set vary significantly when using different isotherms or different nonlinear fitting methods. In this work, the commonly used bi-Langmuir isotherm, which assumes two classes of independent sites, is confronted with the Sips isotherm. Direct, log-log, and Scatchard plots are used, and the interpretation of the binding curves in the latter is critically a…

Drugmedia_common.quotation_subjectBinding energyPharmaceutical Science02 engineering and technology010402 general chemistryBioinformatics01 natural sciencesInterpretation (model theory)MelaninGoodness of fitMelanin bindingFitting methodsDrug Discoverymedia_commonMelaninsScatchard plotChemistrytechnology industry and agricultureChloroquineModels Theoretical021001 nanoscience & nanotechnology0104 chemical sciencesKineticsBiophysicsMolecular Medicine0210 nano-technologyMetoprololMolecular Pharmaceutics
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Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer

2021

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based…

sif010504 meteorology & atmospheric sciencesprincipal component analysisComputer scienceSciencesun-induced fluorescenceMultispectral image0211 other engineering and technologiesImaging spectrometeremulation02 engineering and technology01 natural sciencesRobustness (computer science)emulation; machine learning; sun-induced fluorescence; sif; spectral fitting method (sfm); principal component analysis021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingEmulationDimensionality reductionQHyperspectral imagingspectral fitting method (sfm)machine learningPrincipal component analysisRadianceGeneral Earth and Planetary Sciencesddc:620Remote Sensing
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Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecos…

2019

International audience; Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluore…

Forest ecosystems[SDV.SA]Life Sciences [q-bio]/Agricultural sciences010504 meteorology & atmospheric sciencesFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTRE0208 environmental biotechnologyGEO/04 - GEOGRAFIA FISICA E GEOMORFOLOGIASpectral fitting methodSoil Science02 engineering and technology01 natural sciencesArticleCarbon cycleGEO/11 - GEOFISICA APPLICATAAtmospheric radiative transfer codesAirborne spectroscopyForest ecologySun-induced chlorophyll fluorescenceddc:550LUEEcosystemAPARSun-induced chlorophyll fluorescenceSpectral fitting methodPlant traitsINFORMGPPAPARLUEBESSForest ecosystemsHyPlantAirborne spectroscopyComputers in Earth SciencesChlorophyll fluorescenceBESS0105 earth and related environmental sciencesRemote sensingPlant traitsINFORMGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERAGeology15. Life on land020801 environmental engineeringSpatial heterogeneityGEO/10 - GEOFISICA DELLA TERRA SOLIDA13. Climate actionHyPlantEnvironmental scienceSpatial variabilityGPPScale (map)
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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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Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun–Induced Chlorophyll Fluorescence

2018

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O 2 ) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (…

1171 GeosciencesFLUXspectral fitting method (SFM)AIRBORNE010504 meteorology & atmospheric sciencesScience0211 other engineering and technologiesFlux02 engineering and technologyfraunhofer line discriminator (FLD)Surface pressure01 natural sciencesO2 transmittanceAtmospheric radiative transfer codesatmospheric pressureFIELD SPECTROSCOPYTransmittanceAstrophysics::Solar and Stellar AstrophysicsSPACESpectral resolutionAbsorption (electromagnetic radiation)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingproximal sensing4112 Forestrysun-induced chlorophyll fluorescence (SIF)Atmospheric pressureSTRESS DETECTIONPHOTOSYNTHESISQAtmospheric correctionO-2 transmittanceair temperatureREFLECTANCEsun–induced chlorophyll fluorescence (SIF)Physics::Space Physicssun–induced chlorophyll fluorescence (SIF); proximal sensing; O<sub>2</sub> transmittance; fraunhofer line discriminator (FLD); spectral fitting method (SFM); air temperature; atmospheric pressureLUMINESCENCEGeneral Earth and Planetary SciencesEnvironmental scienceABSORPTION-BANDSAstrophysics::Earth and Planetary AstrophysicsVEGETATIONRemote Sensing
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Retrieval of sun-induced fluorescence using advanced spectral fitting methods

2015

Abstract The FLuorescence EXplorer (FLEX) satellite mission, candidate of ESA's 8th Earth Explorer program, is explicitly optimized for detecting the sun-induced fluorescence emitted by plants. It will allow consistent measurements around the O2-B (687 nm) and O2-A (760 nm) bands, related to the red and far-red fluorescence emission peaks respectively, the photochemical reflectance index, and the structural-chemical state variables of the canopy. The sun-induced fluorescence signal, overlapped to the surface reflected radiance, can be accurately retrieved by employing the powerful spectral fitting technique. In this framework, a set of fluorescence retrieval algorithms optimized for FLEX ar…

PhysicsMETIS-314125Spectrometerbusiness.industryRetrieval algorithmSpectral fitting methodSoil ScienceGeologyFull fluorescence spectrumPhotochemical Reflectance IndexFluorescenceSpectral linen/a OA procedureFLEX missionOpticsSun-induced fluorescenceRadianceRadiative transferSatelliteComputers in Earth SciencesbusinessAbsorption (electromagnetic radiation)Remote sensing
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Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

2021

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from $10^{17}~$eV up to more than $10^{20}~$eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightfo…

PhotonPhysics::Instrumentation and DetectorsAstronomyElectron01 natural sciencesHigh Energy Physics - ExperimentAugerHigh Energy Physics - Experiment (hep-ex)mass [cosmic radiation]surface [detector]Observatory[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]photon: cosmic radiationInstrumentationMathematical PhysicsPhysicsAGASAPhysicsSettore FIS/01 - Fisica SperimentaleDetectorcosmic radiation [photon]Astrophysics::Instrumentation and Methods for AstrophysicsMonte Carlo [numerical calculations]electromagnetic [showers]Augerobservatorycosmic radiation [electron]Analysis and statistical methodsnumerical calculations: Monte CarloAnalysis and statistical methodperformancepositron: cosmic radiationatmosphere [showers]Cherenkov detectordata analysis methodAnalysis and statistical methods; Calibration and fitting methods; Cherenkov detectors; Cluster finding; Large detector systems for particle and astroparticle physics; Pattern recognitionCherenkov counter: waterairneural networkAstrophysics::High Energy Astrophysical Phenomena610FOS: Physical sciencesCosmic raycosmic radiation [positron]cosmic radiation: massCalibration and fitting methodNuclear physicsstatistical analysisPattern recognition0103 physical sciencesshowers: electromagneticddc:530ddc:610High Energy Physics010306 general physicsZenithPierre Auger ObservatoryCalibration and fitting methodscosmic radiation [muon]Muonshowers: atmosphere010308 nuclear & particles physicsdetector: surfacehep-exLarge detector systems for particle and astroparticle physicswater [Cherenkov counter]Cherenkov detectorsCluster findingelectron: cosmic radiationRecurrent neural networkmuon: cosmic radiationLarge detector systems for particle and astroparticle physicExperimental High Energy PhysicsHigh Energy Physics::ExperimentRAIOS CÓSMICOSexperimental results
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Electron and photon energy calibration with the ATLAS detector using 2015-2016 LHC proton-proton collision data

2019

Artículo realizado por muchos autores. Solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración y los autores que firman como pertenecientes a la UAM

Z0 --&gt; electron positronJ/psi(3100) --> electron positronProton13000 GeV-cmsparticle identification [electron]ElectronZ0 --> electron positronelectron: transverse momentum01 natural sciencesphoton: particle identificationSubatomär fysik0302 clinical medicinescattering [p p]Nuclear Experiment proton–proton collisionsLarge Hadron ColliderCalibration and fittingphoton: transverse momentumand fitting methodsphoton: energy:Mathematics and natural scienses: 400::Physics: 430::Nuclear and elementary particle physics: 431 [VDP]calibration [energy]CERN LHC Collcalibration and fitting methodcolliding beams [p p]transverse momentum [electron]p p: scatteringCiências Naturais::Ciências Físicas610LHC ATLAS High Energy PhysicsPhoton energyFitting methodsJ/psi(3100) --&gt; electron positronradiative decay [J/psi(3100)]Nuclear physicsMomentum03 medical and health sciencesAtlas (anatomy)High Energy Physicspair production [electron]CALORIMETERScience & Technologyradiative decay [Z0]electron: particle identification010308 nuclear & particles physicsenergy [photon]Acceleratorfysik och instrumentering jets energy: calibrationCalorimeter methodExperimental High Energy PhysicsPerformance of High Energy Physics Detectorsp p: colliding beamsacceptancetransverse momentum [photon]PhotonJ/psi(3100): radiative decayCalorimeter methods; Pattern recognition cluster finding calibration; and fitting methods; Performance of High Energy Physics Detectors; PARTON DISTRIBUTIONS; LIQUID AR; CALORIMETER; KR030218 nuclear medicine & medical imagingHigh Energy Physics - Experimentelectron: pair productionHigh Energy Physics - Experiment (hep-ex)Subatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Collisions Calorimeter methodsInstrumentationMathematical PhysicsBosonPhysicsPattern recognition cluster finding calibration and fitting methodsSettore FIS/01 - Fisica Sperimentalecalibration and fitting methodsATLASLIQUID ARmedicine.anatomical_structureKRCalibrationcalibration and fitting methods; Calorimeter methods; cluster finding; Pattern recognition; Performance of High Energy Physics Detectors; Instrumentation; Mathematical PhysicsParticle Physics - Experiment530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciencesZ0: radiative decayAccelerator Physics and Instrumentationcalibration and fitting methods; Calorimeter methods; cluster finding; Pattern recognition; Performance of High Energy Physics DetectorsPattern recognition0103 physical sciencesmedicineddc:610hep-exCluster finding:Matematikk og naturvitenskap: 400::Fysikk: 430::Kjerne- og elementærpartikkelfysikk: 431 [VDP]particle identification [photon]FísicaPARTON DISTRIBUTIONSHigh Energy Physics::Experimentexperimental results
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Performance of $b$-Jet Identification in the ATLAS Experiment

2016

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT an…

detector-systems performancePerformance of High Energy Physics Detectorsecondary [vertex]Elementary particle01 natural sciencesPARTONlaw.inventionSubatomär fysikCHANNELcluster findingscattering [p p]impact parameterGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)протон-протонные столкновенияQBLarge detector-systems performanceHigh energy physics detectorLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; Instrumentation; Mathematical Physicstrack data analysisQUARK PAIR PRODUCTIONbottom [jet]CERN LHC CollPattern recognition cluster finding calibration and fitting method7000 GeV-cmscolliding beams [p p]performanceHADRONIC COLLISIONSCiências Naturais::Ciências FísicasLarge detectorFitting methodHigh energy physicATLAS LHC High Energy Physics510 MathematicsmuonDISTRIBUTIONSUncertainty analysis Astroparticle physicHigh Energy Physics010306 general physicsSystematic uncertainties AlgorithmsAstroparticle physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsParticle acceleratorRangingPerformance of High Energy PhysicsCOLLIDERScorrelationExperimental High Energy PhysicsPerformance of High Energy Physics DetectorshadronATLAS детекторБольшой адронный коллайдерcharm [jet]Elementary particleHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental TechniquesInstrumentationUncertainty analysisMathematical PhysicsPhysicsPattern recognition cluster finding calibration and fitting methods4. EducationATLAS experimentSettore FIS/01 - Fisica SperimentaleDetectorsflavor [jet]calibration and fitting methodsATLASLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large; detector-systems performance; Pattern recognition cluster finding; calibration and fitting methods; Performance of High Energy Physics; Detectors; PRODUCTION CROSS-SECTION; QUARK PAIR PRODUCTION; ROOT-S=7 TEV; PARTON; DISTRIBUTIONS; HADRONIC COLLISIONS; MATRIX-ELEMENTS; LHC; COLLIDERS; DETECTOR; CHANNEL8. Economic growthCalibrationparticle identification [bottom]LHCImpact parameterParticle Physics - ExperimentParticle physicsdata analysis method530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciences530MATRIX-ELEMENTSparticle identification [charm]on-line [trigger]Pattern recognition0103 physical sciencesComplementary methodddc:610DETECTORROOT-S=7 TEVCluster findingFísicaLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics DetectorsPattern recognition systemcalibrationtracksPRODUCTION CROSS-SECTIONefficiencyHadronLarge detector systems for particle and astroparticle physicLargeHigh Energy Physics::ExperimentStatistical correlationstatisticalexperimental results
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