1,331 research outputs found

    Search for heavy resonances decaying into a ZZ boson and a vector boson in the ννˉ\nu \bar{\nu} qqˉq\bar{q} final state at CMS

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    This thesis presents a search for potential signals of new heavy resonances decaying into a pair of vector bosons, with masses between 1 TeV and 4 TeV, predicted by beyond standard model theories. The signals probed are spin-1 W', predicted by the Heavy Vector Triplet model, and spin-2 bulk gravitons, predicted by warped extra-dimension models. The scrutinized data are produced by LHC proton-proton collisions at a center-of-mass energy s=13\sqrt{s}=13 TeV during the 2016 operations, and collected by the CMS experiment, corresponding to an integrated luminosity of 35.9 fbinv. One of the boson should be a Z, and it is identified through its invisible decay into neutrinos, while the other electroweak boson, consisting either into a W or into a Z boson, is required to decay hadronically into a pair of quarks. The decay products of heavy resonances are produced with large Lorentz boosts; as a consequence, the decay products of the bosons (quarks and neutrinos) are expected to be highly energetic and collimated. The couple of neutrinos, escaping undetected, is reconstructed as missing momentum in the transverse plane of the CMS detector. The couple of quarks is reconstructed as one large-cone jet, with high transverse momentum, recoiling against the couple of neutrinos. Grooming algorithms are adopted in order to improve the jet mass resolution, by removing soft radiation components and spectator events from the particles clustered as the large-cone jet. The groomed jet mass is used to tag the hadronically decaying vector boson, to define the signal region of the search (close to the nominal mass of the W and Z bosons, between 65-105 GeV) and a signal-depleted control region, that is used for the background estimation. An hybrid data-simulation approach predicts the normalization and the shape of the main background, represented by a vector boson produced in association with jets, by taking advantage of the distribution of data in the signal-depleted control regions. Secondary backgrounds are predicted from simulations. Jet substructure techniques are exploited, in order to classify events into two exclusive purity categories, by distinguishing the couple of quarks inside the large-cone jet. This approach improves the background rejection and the discovery reach. The search is performed by scanning the distribution of the reconstructed mass of the resonance, looking for a local excess in data with regards to the prediction. Depending on the mass, upper limits on the cross-section of heavy spin-1 and spin-2 narrow resonances, multiplied by the branching fraction of the resonance decaying into Z and a W boson for a spin-1 signal, and into a pair of Z bosons for spin-2, are set in the range 0.90.9 -- 6363 fb and in the range 0.50.5 -- 4040 fb respectively. A W' hypothesis is excluded up to 3.11 TeV, in the Heavy Vector Triplet benchmark A scenario, and up to 3.41 TeV, considering the benchmark B scenario. A bulk graviton hypothesis, given the curvature parameter of the extra-dimension k~=1.0\tilde{k}=1.0, is excluded up to 1.14 TeV

    Ricerca di nuova fisica a CMS in eventi con leptoni nello stato finale

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    Questa tesi presenta l'analisi mirata alla scoperta di un eventuale bosone Z' di massa elevata (M>2TeV) nei processi di interazione protone-protone, con il fine di ottimizzare la ricerca nei dati che verranno raccolti da CMS a partire dal 2015, quando il funzionamento di LHC porterà l'energia del centro di massa a 13 TeV. Nella tesi si sono rianalizzati i dati ad 8 TeV per riprodurre i risultati pubblicati. La ricerca, effettuata nel canale Z'→μ+μ-, non mostra nessun eccesso di eventi rispetto ai fondi attesi.ope

    Shared Data and Algorithms for Deep Learning in Fundamental Physics

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    We introduce a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.Comment: 13 pages, 5 figures, 5 table

    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

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