In this thesis a measurement of the differential cross section of top quark pair production in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV is presented. The measurement is performed with data collected in 2018 using the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), corresponding to an integrated luminosity of 59.83 fb-1. The analysis is performed using the dileptonic different-flavor e-mu decay channel. The cross section is measured differentially as a function of the invariant mass of the top quark pair system. The presence of two final state neutrinos makes a kinematic reconstruction necessary for the measurement of m(ttbar). In this work, the observable m(ttbar) is regressed from the visible detector objects by a neural network, leading to an improvement in the efficiency, resolution and a reduction of the statistical uncertainties in the unfolded cross section compared to results based on analytical reconstruction approaches.

In this thesis a measurement of the differential cross section of top quark pair production in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV is presented. The measurement is performed with data collected in 2018 using the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), corresponding to an integrated luminosity of 59.83 fb-1. The analysis is performed using the dileptonic different-flavor e-mu decay channel. The cross section is measured differentially as a function of the invariant mass of the top quark pair system. The presence of two final state neutrinos makes a kinematic reconstruction necessary for the measurement of m(ttbar). In this work, the observable m(ttbar) is regressed from the visible detector objects by a neural network, leading to an improvement in the efficiency, resolution and a reduction of the statistical uncertainties in the unfolded cross section compared to results based on analytical reconstruction approaches.

Differential cross section measurement of top quark pair production with the CMS experiment using a ML-based kinematic reconstruction

CELOTTO, GIOVANNI
2022/2023

Abstract

In this thesis a measurement of the differential cross section of top quark pair production in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV is presented. The measurement is performed with data collected in 2018 using the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), corresponding to an integrated luminosity of 59.83 fb-1. The analysis is performed using the dileptonic different-flavor e-mu decay channel. The cross section is measured differentially as a function of the invariant mass of the top quark pair system. The presence of two final state neutrinos makes a kinematic reconstruction necessary for the measurement of m(ttbar). In this work, the observable m(ttbar) is regressed from the visible detector objects by a neural network, leading to an improvement in the efficiency, resolution and a reduction of the statistical uncertainties in the unfolded cross section compared to results based on analytical reconstruction approaches.
2022
Differential cross section measurement of top quark pair production with the CMS experiment using a ML-based kinematic reconstruction
In this thesis a measurement of the differential cross section of top quark pair production in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV is presented. The measurement is performed with data collected in 2018 using the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), corresponding to an integrated luminosity of 59.83 fb-1. The analysis is performed using the dileptonic different-flavor e-mu decay channel. The cross section is measured differentially as a function of the invariant mass of the top quark pair system. The presence of two final state neutrinos makes a kinematic reconstruction necessary for the measurement of m(ttbar). In this work, the observable m(ttbar) is regressed from the visible detector objects by a neural network, leading to an improvement in the efficiency, resolution and a reduction of the statistical uncertainties in the unfolded cross section compared to results based on analytical reconstruction approaches.
Top quark
Standard Model
CMS
Cross section
Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/52992