335 research outputs found

    Search for long-lived heavy neutral leptons with the CMS experiment using machine learning techniques

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    This Thesis presents the analysis strategies and outcomes of a search looking for long-lived Heavy Neutral Leptons (HNLs) in proton-proton collision data corresponding to 137fb^{-1} integrated luminosity, collected by the CMS experiment at the Large Hadron Collider with \sqrt{s}=13 TeV. The HNL particles are searched for via charged-current production and semi-leptonic decay mode into a lepton and jet that can be displaced from the primary collision point. A fine-grained event categorisation scheme ensures enhanced sensitivity to a number of HNL benchmark models including both Dirac- and Majorana-type particles. The search employs a novel deep neural network-based displaced jet tagger and a complementary event-level boosted decision tree algorithm with the aim of separating HNL signal events from Standard Model (SM) background processes. The blinded analysis uses a data-driven background estimation method to evaluate the expected yields, the method being validated in two control regions. After unblinding, no excess of events was found and upper limits on the HNL production cross-section were set for a broad range of HNL mass, lifetime and coupling scenarios, including universal mixing with all SM lepton generations. A particular focus of this Thesis was the evaluation of displaced lepton reconstruction and identification of scale factors, which were found to be one of the dominant systematic uncertainties associated with the signal interpretation. The results presented in this Thesis are competitive with previously published similar searches in high energy physics and are the first in the CMS collaboration to consider arbitrary coupling combinations involving all three SM lepton generations

    Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System

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    The CMS experiment will be upgraded to maintain physics sensitivity and exploit the higher luminosity of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary vertex finding which is used to identify the hard scatter in an event and separate the primary interaction from additional simultaneous interactions. This work presents a novel approach to regress the primary vertex position and to reject tracks from additional soft interactions, which uses an end-to-end neural network. This neural network possesses simultaneous knowledge of all stages in the reconstruction chain, which allows for end-to-end optimisation. The improved performance of this network versus a baseline approach in the primary vertex regression and track-to-vertex classification is shown. A quantised and pruned version of the neural network is deployed on an FPGA to match the stringent timing and computing requirements of the Level-1 Trigger

    Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

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    International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum pTp_\mathrm{T} and rapidity yy. The measurement is performed using proton-proton collision data at s\sqrt{s} = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb‚ąí1^{-1}. The jets are reconstructed with the anti-kTk_\mathrm{T} algorithm using a distance parameter of RR = 0.4, within the rapidity interval ‚ą£y‚ą£\lvert y\rvert<\lt 2, and across the kinematic range 0.06 <\ltpTp_\mathrm{T}<\lt 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling őĪS\alpha_\mathrm{S}

    Measurement of inclusive and differential cross sections for single top quark production in association with a W boson in proton-proton collisions at s \sqrt{s} = 13 TeV