1,051 research outputs found

    Laboratory and testbeam results for thin and epitaxial planar sensors for HL-LHC

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    The High-Luminosity LHC (HL-LHC) upgrade of the CMS pixel detector will require the development of novel pixel sensors which can withstand the increase in instantaneous luminosity to L = 5 × 1034 cm–2s–1 and collect ~ 3000fb–1 of data. The innermost layer of the pixel detector will be exposed to doses of about 1016 neq/ cm2. Hence, new pixel sensors with improved radiation hardness need to be investigated. A variety of silicon materials (Float-zone, Magnetic Czochralski and Epitaxially grown silicon), with thicknesses from 50 μm to 320 μm in p-type and n-type substrates have been fabricated using single-sided processing. The effect of reducing the sensor active thickness to improve radiation hardness by using various techniques (deep diffusion, wafer thinning, or growing epitaxial silicon on a handle wafer) has been studied. Furthermore, the results for electrical characterization, charge collection efficiency, and position resolution of various n-on-p pixel sensors with different substrates and different pixel geometries (different bias dot gaps and pixel implant sizes) will be presented

    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

    An embedding technique to determine ττ backgrounds in proton-proton collision data