343 research outputs found

    Model independent measurements of Standard Model cross sections with Domain Adaptation

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    With the ever growing amount of data collected by the ATLAS and CMS experiments at the CERN LHC, fiducial and differential measurements of the Higgs boson production cross section have become important tools to test the standard model predictions with an unprecedented level of precision, as well as seeking deviations that can manifest the presence of physics beyond the standard model. These measurements are in general designed for being easily comparable to any present or future theoretical prediction, and to achieve this goal it is important to keep the model dependence to a minimum. Nevertheless, the reduction of the model dependence usually comes at the expense of the measurement precision, preventing to exploit the full potential of the signal extraction procedure. In this paper a novel methodology based on the machine learning concept of domain adaptation is proposed, which allows using a complex deep neural network in the signal extraction procedure while ensuring a minimal dependence of the measurements on the theoretical modelling of the signal.Comment: 16 pages, 10 figure

    Higgs boson cross section measurements at CMS

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    Inclusive and differential measurements of the Higgs boson production cross section are important tools to test the standard model expectations and to probe physics beyond the standard model. This review summarizes recent Higgs boson cross section measurements, obtained in proton-proton collision data, recorded by the CMS experiment at the CERN LHC during Run 2 at s=13\sqrt{s}=13 TeV

    (Re)interpretation of the LHC results for new physics

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    With the ever growing amount of data collected by the ATLAS and CMS experiments at the CERN LHC, fiducial and differential measurements of the Higgs boson production cross section have become important tools to test the Standard Model predictions with an unprecedented level of precision, as well as seeking deviations that can manifest the presence of physics beyond the standard model. These measurements are in general designed for being easily comparable to any present or future theoretical prediction, and to achieve this goal it is important to keep the model dependence to a minimum. Nevertheless, the reduction of the model dependence usually comes at the expense of the measurement precision, preventing to exploit the full potential of the signal extraction procedure. In this talk a novel methodology based on the machine learning concept of domain adaptation is proposed, which allows using a complex deep neural network in the signal extraction procedure while ensuring a minimal dependence of the measurements on the theoretical modelling of the signal

    Model independent measurements of standard model cross sections with domain adaptation

    No full text
    With the ever growing amount of data collected by the ATLAS and CMS experiments at the CERN LHC, fiducial and differential measurements of the Higgs boson production cross section have become important tools to test the standard model predictions with an unprecedented level of precision, as well as seeking deviations that can manifest the presence of physics beyond the standard model. These measurements are in general designed for being easily comparable to any present or future theoretical prediction, and to achieve this goal it is important to keep the model dependence to a minimum. Nevertheless, the reduction of the model dependence usually comes at the expense of the measurement precision, preventing to exploit the full potential of the signal extraction procedure. In this paper a novel methodology based on the machine learning concept of domain adaptation is proposed, which allows using a complex deep neural network in the signal extraction procedure while ensuring a minimal dependence of the measurements on the theoretical modeling of the signal

    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}

    Observation of four top quark production in proton-proton collisions at s\sqrt{s} = 13 TeV