656 research outputs found

    Jet Measurements In CMS

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    A measurement of inclusive jet and dijet production cross sections is presented. Data from large hadron collider (LHC) proton-proton collisions at s=\sqrt{s}= 7 TeV, corresponding to 4.67fb‚ąí14.67 fb^{-1} of integrated luminosity, have been collected with the compact muon solenoid (CMS) detector. Jets are reconstructed with the anti-kTk_T clustering algorithm with size parameter R=0.7R=0.7, extending to rapidity ‚ą£y‚ą£=2.5|y|=2.5, transverse momentum pT=p_{T}= 2 TeV, and dijet invariant mass MJJ=M_{JJ}= 5 TeV. The measured cross sections are corrected for detector effects and compared to perturbative QCD predictions at next-to-leading order (NLO), corrected for non perturbative (NP) factors, using various sets of parton distribution functions. Determination Of Jet Energy Correction from s=\sqrt{s}= 7 TeV CMS data is presented. The individual components are determined. The jet energy scale uncertainty factors are also shown.Comment: 6 pages, 5 figures. Proceedings For ICHEP'201

    Jet Production Measurements at CMS

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    Jet production cross-section measurements are presented. The measurements are done with the data from Large Hadron Collider (LHC) proton-proton collisions, collected with the Compact Muon Solenoid (CMS) detector. The inclusive jet production measurements are carried out with data collected s¬†=¬†7¬†TeV\rm \sqrt{s} ~= ~7 ~TeV and 8¬†TeV\rm 8~TeV with total integrated luminosity (Lint\mathcal{L}_{int}) 5.0¬†fb‚ąí1\rm 5.0~ fb^{-1} and 10.71¬†fb‚ąí1\rm 10.71~ fb^{-1} respectively. The dijet production measurements are carried out with the s¬†=¬†7¬†TeV\rm \sqrt{s}~ =~ 7 ~TeV dataset. Jets are reconstructed with the anti-kTk_T clustering algorithm with size parameter R=0.7R=0.7. The measured cross sections are corrected for detector effects and compared to perturbative QCD predictions at NLO, corrected for NP factors, using various sets of PDF. The inclusive jet cross-section ratio of the jets reconstructed with the anti-kTk_T (AK) algorithm and two radius parameter R¬†=¬†0.5\rm R~=~0.5 and R¬†=¬†0.7\rm R~=~0.7 are also presented. The data used is s¬†=¬†7¬†TeV\rm \sqrt{s}~ =~ 7 ~TeV CMS data corresponding to Lint¬†=¬†5.0¬†fb‚ąí1\rm \mathcal{L}_{int}~=~5.0 ~fb^{-1}. Significant discrepancies are found comparing the data to leading order calculations and to fixed order calculations at NLO, corrected for NP effects, whereas simulations with NLO matrix elements matched to the parton showers describe the data quite well. A study of color coherence effects in pp collisions has been performed with the data collected at s¬†=¬†7¬†TeV\rm \sqrt{s}~ =~ 7~TeV and Lint¬†=¬†36¬†pb‚ąí1\rm\mathcal{L}_{int}~=~ 36~pb^{-1}. The measurement of the azimuthal angular correlation between the second and third jets is compared to the predictions of Monte Carlo models with different implementations of color coherence effects.Comment: 8 pages, 6 figures. Proceedings for EPS-HEP 201

    Secondary Vertex Finding in Jets with Neural Networks

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    Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance

    Reconstructing particles in jets using set transformer and hypergraph prediction networks

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    The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow, which benefits from a physically-interpretable approach to particle reconstruction.Comment: 17 pages, 21 figure

    Light quark Yukawas in triboson final states

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    Abstract Triple heavy vector boson production, pp ‚Üí VVV (V = W, Z), has recently been observed for the first time. We propose that precision measurements of this process provide an excellent probe of the first generation light quark Yukawa couplings. Modified quark interactions with the off-shell Higgs in this process lead to a rapid growth of the partonic cross sections with energy, which manifests in an enhanced pT distribution of the final state leptons and quarks. We quantify this effect and estimate the present and future 2ŌÉ sensitivity to the up, down, and strange Yukawas. In particular, we find that HL-LHC can reach O(400) \mathcal{O}(400) O 400 sensitivity to the down Yukawa relative to the Standard Model value, improving the current sensitivity in this process by a factor of 10, and which can be further improved to O(30) \mathcal{O}(30) O 30 at FCC-hh. This is competitive with and complementary to constraints from global fits and other on-shell probes of the first generation Yukawas. The triboson sensitivity at HL-LHC corresponds to probing dimension-6 SMEFT operators suppressed by an O(1) \mathcal{O}(1) O 1 TeV scale, similarly to other LHC Higgs probes.</jats:p

    Differential cross section measurements for the production of a W boson in association with jets in proton‚Äďproton collisions at ‚ąös = 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 ‚ąí1]. 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

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