Precision meets sustainability at the LHC: extraction of fundamental parameters of the Standard Model and machine learning techniques in the simulation / vorgelegt von Valentina Guglielmi

Guglielmi, Valentina

This thesis presents precise determinations of two fundamental parameters of the standard model (SM), the strong coupling constant αS(mZ) and the top quark mass mt along with its width Γt. These contribute importantly to constraints on the stability of the SM electroweak vacuum. For the determination of αS(mZ), CMS inclusive jet measurements using LHC proton-proton (pp) collisions at centre-of-mass energies √s= 2.76, 7, 8, and 13 TeV are analysed together, for the first time. This has been made possible by detailed studies of the correlations between the different data sets. The CMS jet data are combined with HERA deep inelastic scattering measurements to extract the parton distribution functions (PDFs) and αS(mZ), simultaneously. This approach properly accounts for the correlation between PDFs and αS(mZ). The resulting value, αS(mZ) = 0.1176+0.0014−0.0016, is the most precise value of αS(mZ) from jet rates to date and has been achieved through a comprehensive QCD analysis at next-to-next-to-leading order. Further, the running of αS up to an energy scale of 1.6 TeV is probed. The measurement of the top quark mass parameter in the Monte Carlo (MC) simulation mMCt and Γt from the unfolded differential cross section of top quark-antiquark(tt) and single top quark production in association with a W boson (tW) is performed. This analysis uses LHC pp collision data at √s = 13 TeV, collected by the CMS experiment during 2017–2018. Events in the dilepton decay channel are selected.The differential cross section as a function of the invariant mass of the lepton and b quark, mℓb, is unfolded to the particle level. This analysis is the first of its kind using CMS data, employing the state-of-the-art event generator bb4l, which simulates ppbbℓ+ℓ−ν¯ν final states and takes into account the interference between tt and tW production. The precision of this measurement is estimated using Asimov pseudo-data, resulting in mMCt = 172.61+0.41−0.44 GeV and Γt = 1.36+0.24−0.28 GeV. The mMCt result is as precise as the most accurate single-experiment direct mMCt measurement and will present the first determination of mMCt from the combined tt and tW cross sectionsfrom the CMS Collaboration once the analysis is unblinded. Further, this analysis promises improved precision as compared to direct measurements of Γt obtained with the bb4l method. Finally, a machine learning (ML) technique is presented, developed to reweight MC simulations obtained with a particular set of model parameters to simulations with alternative values of these parameters, or to simulations based on an entirely different model. The reweighting is performed at the generator level by applying the output of the ML algorithm, stored as weights, to the nominal MC simulation. As a result, detailed detector simulation and event reconstruction are not needed for alternative MC samples, significantly reducing computational costs by up to 75%. The performance of the method is studied in simulated tt production and results are presented for reweighting to model variations and higher-order calculations. This ML-based reweighting is already used by the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.

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