1,364 research outputs found

    Exclusive production observed at the CMS experiment

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    Exclusive W+^{+}W−^{-} pair production in photon-photon collisions during the pp runs at 7 and 8 TeV are observed and used to put constraints on the Anomalous Quartic Gauge Couplings. During the proton lead collisions in photon-induced vector meson production is observed via the decay of upsilon into two muons. The slope of the squared pT_{\rm T} distribution is measured to determine the size of the production region.Comment: 8 pages, 6 figures, to appear in the proceedings of DIS201

    Measurement of Diffractive and Exclusive processes

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    With excellent performance the Compact Muon Solenoid (CMS) experiment has made a number of key observations in the diffractive and exclusive processes and hence in probing the Standard model in a unique way. This presentation will cover recent results on the measurement of diffractive and exclusive processes using data recorded by CMS detector at the LHC.Comment: 4pages, 6 figures, ICHEP 2018, International Conference on High Energy Physics, 4-11 July 2018, Seoul, South Kore

    End-to-end deep learning inference with CMSSW via ONNX using docker

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    Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep learning approach to identify various particles using low-level detector information from high-energy collisions. These models will be incorporated in the CMS software framework (CMSSW) to enable their use for particle reconstruction or for trigger operation in real-time. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports an implementation of the end-to-end deep learning inference with the CMS software framework. The inference has been implemented on GPU for faster computation using ONNX. We have benchmarked the ONNX inference with GPU and CPU using NERSCs Perlmutter cluster by building a docker image of the CMS software framework.Comment: 9 pages, 7 figures, CHEP2023 proceedings, submitted to EPJ Web of Conference

    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 ð„ „ ðandðŽð„ = 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

    Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks