Search for new particles decaying to top quark pairs with the CMS experiment

Abstract

In this thesis a search for new particles decaying to top quark pairs is presented. The analysis is based on proton-proton collisions recorded with the CMS experiment at 13 TeV in the years 2016-2018, corresponding to an integrated luminosity of 138 fb−1. Many theories of physics Beyond the Standard Model predict the existence of new particles that modify the ttbar mass spectrum and could explain some of the shortcomings of the Standard Model, connected for example to the hierarchy problem and the electroweak symmetry breaking. The models considered in this thesis include spin-1 particles, i.e. Z′ bosons and gKK gluons at the multi-TeV scale, as well as spin-0 particles, scalar (H) or pseudoscalar (A) heavy Higgs bosons at masses up to 1 TeV. The heavy Higgs signals present interference with the ttbar background, resulting in a peak-dip structure in the ttbar invariant mass spectrum, while spin-1 particles manifest themselves as peaks. The search is performed in the final state with a muon or an electron, jets and missing transverse momentum. Both the resolved and the boosted final state topologies are probed. In particular, novel machine-learning algorithms are used to identify the hadronic decay of the top quark in the highly Lorentz-boosted regime, where its decay products are collimated. Furthermore, a deep neural network for event classification is applied to categorize the events in the main backgrounds. Upper limits are placed on the production cross section of new spin-1 particles: masses up to 4.3 TeV, 5.3 TeV and 6.7 TeV are excluded for Z′ bosons with 1%, 10% and 30% relative widths, respectively, and up to 4.7 TeV for gKK gluons. Moreover, exclusion limits are placed on the coupling strength modifiers of H and A bosons for masses in the range 365-1000 GeV and 2.5% relative width. The high instantaneous luminosity reached by the LHC in Run 2 leads to a high number of additional pp interactions in the same bunch crossing (pileup). It is fundamental to identify the interaction of interest in each event and to mitigate the effects of pileup on the object reconstruction. The PUPPI algorithm, used in CMS since Run 2, shows the best performance and it is the default algorithm in CMS for Run 3 and beyond. The new version of the algorithm for the re-reconstruction of Run 2 data, the Ultra Legacy (UL) reconstruction, is presented in this thesis. The new tune, PUPPI v15, features an improved track-vertex association that leads to an improved jet energy and pTmiss resolution. PUPPI v15 is used in all the CMS analysis based on UL Run 2 data that use large-radius jets.