Search for Production of Supersymmetric Top Quarks in Hadronic and Multi-leptonic Final States, Using a Deep Neural Network Based Soft B-tagger For Compressed Mass Scenarios

Abstract

A search is performed for pair produced supersymmetric top (stop) quarks in hadronic andmulti-leptonic final states. The search uses a sample of proton-proton collision data at p s = 13 TeV, corresponding to 137 fb?1, recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). The searches are focused on events with a high transverse momentum system from initial-state-radiation jets recoiling against a potential supersymmetric particle (sparticle) system with significant missing transverse momentum. Stop signals which have small mass splittings between the stop and the lightest supersymmetric particle (LSP) on the order of 10s of GeV are studied for stop masses ranging from 400 to 1500 GeV. This dissertation probes the compressed mass phase space through the use of Recursive Jigsaw Reconstruction (RJR) by assigning reconstructed objects to the initial state radiation or sparticle system following a generic decay tree, and using this assignment to take advantage of mass sensitive variables in different rest frames. A new Deep Neural Network based b quark tagger has been developed to find low pT b quarks using secondary vertices. The signal regions are defined by the multiplicity of reconstructed objects in each of the two systems, including leptons, jets, soft b-tagged secondary vertices, and b-tagged jets. Limits are placed on the pair production of stops quarks and are interpreted within the framework of simplified models. Exclusions at 95% Confidence Level (CL) are expected for stop masses up to 675 GeV for neutralino masses up to 665 GeV, where the neutralino is assumed to be the lightest supersymmetric particle. The last part of the dissertation details a project, independent of the stop search, which looks at calculating the location of the CMS beam spot using tracking independent methods. A method was developed, making use of a maximum likelihood fit, which only uses the cluster occupancy and x, y, and z positions of the read out chips located in the first layer of the barrel pixel detector, and is accurate to within 1 mm of the true beam spot when tested on simulated Monte Carlo (MC).