Search results for "Mathematical software"

showing 10 items of 60 documents

Neutral-Current Neutrino-Nucleus Scattering off Xe Isotopes

2018

Large liquid xenon detectors aiming for dark matter direct detection will soon become viable tools also for investigating neutrino physics. Information on the effects of nuclear structure in neutrino-nucleus scattering can be important in distinguishing neutrino backgrounds in such detectors. We perform calculations for differential and total cross sections of neutral-current neutrino scattering off the most abundant xenon isotopes. The nuclear structure calculations are made in the nuclear shell model for elastic scattering, and also in the quasiparticle random-phase approximation (QRPA) and microscopic quasiparticle phonon model (MQPM) for both elastic and inelastic scattering. Using suit…

Computer Science::Machine LearningNuclear and High Energy PhysicsArticle SubjectNuclear TheoryPhysics::Instrumentation and DetectorsSolar neutrinoAstrophysics::High Energy Astrophysical PhenomenaDark matterNuclear TheoryFOS: Physical sciencesInelastic scatteringComputer Science::Digital Libraries01 natural sciencesNuclear Theory (nucl-th)Nuclear physicsStatistics::Machine LearningHigh Energy Physics - Phenomenology (hep-ph)neutrino physics0103 physical sciencesIsotopes of xenonsironta010306 general physicsPhysicsElastic scatteringneutrino-nucleus scatteringta114010308 nuclear & particles physicsScatteringHigh Energy Physics::PhenomenologyNuclear shell modelneutriinotlcsh:QC1-999High Energy Physics - PhenomenologyComputer Science::Mathematical SoftwareHigh Energy Physics::ExperimentNeutrinolcsh:PhysicsAdvances in High Energy Physics
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A simple equation for determining sea surface emissivity in the 3–15 µm region

2009

The high level of accuracy demanded for the sea surface temperature retrieval from infrared data requires an accurate determination of directional sea surface emissivity (SSE). Previous models have permitted calculating SSEs using a physical characterization of sea surface roughness and emission. However, these result in complex equations, and make an operational application difficult. This paper presents a simple SSE algorithm based on a parametrization of one of these models, which was selected as a reference since it reproduces SSE experimental data to a reasonable level of accuracy. The parametrization provides the SSE variation with observation angle and wind speed from a given nadir S…

Physics::Computational PhysicsPhysicsAATSRSurface finishWind speedComputer Science::PerformanceComputer Science::Mathematical SoftwareNadirEmissivitySurface roughnessGeneral Earth and Planetary SciencesRadiometryParametrizationRemote sensingInternational Journal of Remote Sensing
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Thermodynamics of the Classical Planar Ferromagnet Close to the Zero-Temperature Critical Point: A Many-Body Approach

2012

We explore the low-temperature thermodynamic properties and crossovers of ad-dimensional classical planar Heisenberg ferromagnet in a longitudinal magnetic field close to its field-induced zero-temperature critical point by employing the two-time Green’s function formalism in classical statistical mechanics. By means of a classical Callen-like method for the magnetization and the Tyablikov-like decoupling procedure, we obtain, for anyd, a low-temperature critical scenario which is quite similar to the one found for the quantum counterpart. Remarkably, ford>2the discrimination between the two cases is found to be related to the different values of the shift exponent which governs the beha…

Computer Science::Machine LearningPhysicsArticle SubjectCondensed matter physicsThermodynamicsStatistical mechanicsCondensed Matter PhysicsComputer Science::Digital Librarieslcsh:QC1-999Statistics::Machine LearningReduced propertiesCritical point (thermodynamics)Critical lineComputer Science::Mathematical SoftwareExponentCritical exponentQuantumlcsh:PhysicsPhase diagramAdvances in Condensed Matter Physics
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Classification of the hadronic decays of the Z$^0$ into b and c quark pairs using a neural network

1992

A classifier based on a feed-forward neural network has been used for separating a sample of about 123 500 selected hadronic decays of the Z 0 , collected by DELPHI during 1991, into three classes according to the flavour of the original quark pair: u u +d d +s s (unresolved), c c and b b . The classification has been used to compute the partial widths of the Z 0 into b and c quark pairs. This gave Γ c c /Γ h = 0.151 ± 0.008 ( stat. ) ± 0.041 ( syst. ) , Γ b b /Γ h = 0.232±0.005 ( stat. )±0.017 ( syst. ) .

QuarkNuclear and High Energy PhysicsParticle physicsLUND MONTE-CARLO; HEAVY FLAVOR PRODUCTION; JET FRAGMENTATION; PHYSICS; BOSONHEAVY FLAVOR PRODUCTIONLUND MONTE-CARLOElectron–positron annihilationFlavourHadronMathematicsofComputing_GENERALComputer Science::Digital Libraries01 natural sciencesJET FRAGMENTATIONCharm quarkPHYSICS0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]010306 general physicsPhysicsArtificial neural network010308 nuclear & particles physicsHigh Energy Physics::PhenomenologyTheoryofComputation_GENERALBOSONMathMLComputer Science::Mathematical SoftwareHigh Energy Physics::ExperimentFísica nuclearClassifier (UML)Particle Physics - Experiment
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JUNO sensitivity to low energy atmospheric neutrino spectra

2021

Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $\nu_e$ and $\nu_\mu$ fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then pro…

Physics and Astronomy (miscellaneous)Physics::Instrumentation and Detectorsscintillation counter: liquidenergy resolutionAtmospheric neutrinoQC770-798Astrophysics7. Clean energy01 natural sciencesneutrino: fluxHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)particle source [neutrino]neutrinoneutrino: atmosphere[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Cherenkovneutrino/e: particle identificationenergy: low [neutrino]Jiangmen Underground Neutrino ObservatoryPhysicsJUNOphotomultiplierliquid [scintillation counter]primary [neutrino]neutrino: energy spectrumDetectoroscillation [neutrino]neutrinosMonte Carlo [numerical calculations]atmosphere [neutrino]QB460-466observatorycosmic radiationComputer Science::Mathematical Softwareproposed experimentNeutrinonumerical calculations: Monte CarloComputer Science::Machine LearningParticle physicsdata analysis methodAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesCosmic rayScintillatorComputer Science::Digital LibrariesNOStatistics::Machine LearningPE2_2neutrino: primaryneutrino: spectrumNuclear and particle physics. Atomic energy. Radioactivity0103 physical sciencesddc:530structure010306 general physicsNeutrino oscillationEngineering (miscellaneous)Cherenkov radiationparticle identification [neutrino/mu]Scintillationneutrino/mu: particle identificationflavordetectorparticle identification [neutrino/e]010308 nuclear & particles physicsneutrino: energy: lowHigh Energy Physics::Phenomenologyspectrum [neutrino]resolutionenergy spectrum [neutrino]flux [neutrino]neutrino: particle source13. Climate actionHigh Energy Physics::Experimentneutrino: oscillationneutrino detector
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Array programming with NumPy.

2020

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programmi…

FOS: Computer and information sciences/639/705/1042Computer science/639/705/794Interoperability/639/705/117Review ArticleStatistics - Computationohjelmointikielet01 natural sciences03 medical and health sciencesSoftwareSoftware Designlaskennallinen tiede0103 physical sciencesFOS: Mathematics010303 astronomy & astrophysicsComputation (stat.CO)030304 developmental biologycomputer.programming_languageSolar physics0303 health sciencesMultidisciplinaryApplication programming interfacebusiness.industryNumPyComputational sciencereview-articleComputational BiologyPython (programming language)Computer science/704/525/870Computational neuroscienceProgramming paradigmSoftware designComputer Science - Mathematical Software/631/378/116/139Programming LanguagesArray programmingohjelmistokirjastotSoftware engineeringbusinessMathematical Software (cs.MS)computerMathematicsSoftwarePythonNature
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PROCEDE DE PRE-DISTORSION NUMERIQUE D’UN SIGNAL ET REPETEUR DE TELECOMMUNICATION INTEGRANT UN FILTRE A REPONSE IMPULSIONNELLE FINIE POUR METTRE EN OE…

2013

L'invention concerne un procédé de pré-distorsion numérique d'un signal de télécommunication traité dans un circuit électronique 100 intégrant un filtre à réponse impulsionnelle finie 321. Ce procédé consiste successivement: - à identifier, à la sortie du circuit 100, les paramètres de distorsions de phase et/ou d'amplitude du signal en fonction de la fréquence, - à partir des susdits paramètres de distorsions relevés, à générer, par un algorithme basé sur une interpolation, des coefficients permettant d'effectuer dans ledit filtre 321, des prédistorsions du signal numérique destinées à engendrer une précorrection des susdites distorsions, - à transférer lesdits coefficients de pré-distorsi…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][SPI.OTHER]Engineering Sciences [physics]/Other[INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR][ SPI.OTHER ] Engineering Sciences [physics]/Other[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Prédistorsion numérique[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsRépéteurs[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS][ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI][SPI.OTHER] Engineering Sciences [physics]/Other[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]Spline[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ INFO.INFO-MS ] Computer Science [cs]/Mathematical Software [cs.MS][INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS]FIR filters[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingFpga
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Measurement of the mass of the W boson using direct reconstruction at √s = 183 GeV

1999

From data corresponding to an integrated luminosity of 53.5 pb(-1) taken during the 183 GeV run in 1997, DELPHI has measured the W mass from direct reconstruction of WW --> lq (q) over bar and WW --> q (q) over bar q (q) over bar events. Combining these channels, a value of m(w) = 80.238 +/- 0.154(stat) +/- 0.035(syst) +/- 0.035(fsi) +/- 0.021 (LEP) GeV/c(2) is obtained, where fsi denotes final state interaction. Combined with the W mass obtained by DELPHI from the WW production cross-section and with the direct measurement at 172 GeV this leads to a measured value of m(w) = 80.270 +/- 0.137(stat) +/- 0.031(syst) +/- 0.030(fsi) +/- 0.021(LEP)GeV/c(2), in good agreement with the Standard Mod…

Nuclear and High Energy PhysicsParticle physicsEINSTEIN CORRELATIONSCLUSTERING-ALGORITHMElectron–positron annihilationMathematicsofComputing_GENERALCOLOR DIPOLE MODEL01 natural sciencesComputer Science::Digital LibrariesPartícules (Física nuclear)LuminosityStandard ModelPHYSICSEVENTSNuclear physicsLEP20103 physical sciencesMONTE-CARLO PROGRAM[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]ANNIHILATION010306 general physicsDELPHIPhysicsAnnihilation010308 nuclear & particles physicsE(+)E(-) INTERACTIONSTheoryofComputation_GENERALLARGE ELECTRON POSITRON COLLIDERMONTE-CARLO PROGRAM; PAIR CROSS-SECTION; COLOR DIPOLE MODEL; E(+)E(-) INTERACTIONS; EINSTEIN CORRELATIONS; CLUSTERING-ALGORITHM; ANNIHILATION; PHYSICS; EVENTS; LEP2PARTICLE PHYSICS; LARGE ELECTRON POSITRON COLLIDER; DELPHIComputer Science::Mathematical SoftwarePARTICLE PHYSICSProduction (computer science)Física nuclearPAIR CROSS-SECTIONParticle Physics - ExperimentBar (unit)
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On solving separable block tridiagonal linear systems using a GPU implementation of radix-4 PSCR method

2018

Partial solution variant of the cyclic reduction (PSCR) method is a direct solver that can be applied to certain types of separable block tridiagonal linear systems. Such linear systems arise, e.g., from the Poisson and the Helmholtz equations discretized with bilinear finite-elements. Furthermore, the separability of the linear system entails that the discretization domain has to be rectangular and the discretization mesh orthogonal. A generalized graphics processing unit (GPU) implementation of the PSCR method is presented. The numerical results indicate up to 24-fold speedups when compared to an equivalent CPU implementation that utilizes a single CPU core. Attained floating point perfor…

Tridiagonal linear systemsProgramvaruteknikComputer Networks and CommunicationsComputer sciencePartial solution techniquereduction010103 numerical & computational mathematicsParallel computingtietotekniikka01 natural scienceslineaariset mallitTheoretical Computer ScienceSeparable spaceinformation technologyArtificial IntelligenceSeparable block tridiagonal linear systemBlock (telecommunications)Fast direct solverRadix0101 mathematicsta113Computer Sciencesta111Linear systemSoftware EngineeringGPU computingSolverComputer Science::Numerical Analysis010101 applied mathematicsPSCR methodDatavetenskap (datalogi)partial solution techniqueHardware and ArchitectureComputer Science::Mathematical Softwarepienennyslinear modelsSoftwareRoofline modelCyclic reductionJournal of Parallel and Distributed Computing
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Factorization of denominators in integration-by-parts reductions

2020

We present a Mathematica package which finds a basis of master integrals for the Feynman integral reduction. In this basis the dependence on the dimensional regularization in the denominators factorizes in kinematic independent polynomials.

High Energy Physics - PhenomenologyHigh Energy Physics - Phenomenology (hep-ph)Computer Science::Mathematical SoftwareFOS: Physical sciencesComputer Science::Symbolic Computation
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