2,329 research outputs found

    Observation of the diphoton decay of the Higgs boson and measurement of its properties

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    Measurement of the prompt J/psi and psi(2S) polarizations in pp collisions at sqrt(s) = 7 TeV

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    The polarizations of prompt J/psi and psi(2S) mesons are measured in proton-proton collisions at sqrt(s) = 7 TeV, using a dimuon data sample collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 4.9 inverse femtobarns. The prompt J/psi and psi(2S) polarization parameters lambda[theta], lambda[phi], and lambda[theta, phi], as well as the frame-invariant quantity lambda(tilde), are measured from the dimuon decay angular distributions in three different polarization frames. The J/psi results are obtained in the transverse momentum range 14 < pt < 70 GeV, in the rapidity intervals abs(y) < 0.6 and 0.6 < abs(y) < 1.2. The corresponding psi(2S) results cover 14 < pt < 50 GeV and include a third rapidity bin, 1.2 < abs(y) < 1.5. No evidence of large transverse or longitudinal polarizations is seen in these kinematic regions, which extend much beyond those previously explored

    Search for narrow resonances in dilepton mass spectra in proton-proton collisions at root s=13 TeV and combination with 8 TeV data

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    Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

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    Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

    Search for top squark pair production in pp collisions at root s=13 TeV using single lepton events

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    Measurement of the triple-differential cross section for photon plus jets production in proton-proton collisions at √s = 7 TeV

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    Search for new physics with dijet angular distributions in proton-proton collisions at root S = 13 TeV

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    Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV

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    Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

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    Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis
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