ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Physics and Applied Physics



First Advisor

Kaushik De

Second Advisor

Giulio Usai


The Standard Model of Particle Physics is the most comprehensive theory describing how fundamental particles and three of the four fundamental forces are related. However, the Standard Model is known to be an incomplete theory with several limitations. Supersymmetry is an extension of the Standard Model of Particle Physics, introducing supersymmetric partners to every fermion and boson in the Standard Model. Supersymmetry gives a diverse collection of theoretical models providing solutions to these phenomenological inconsistencies. It contains a mechanism for stabilizing the Higgs boson mass while predicting the existence of several new particles, including a suitable Dark Matter candidate. The Large Hadron Collider (LHC) is the world's most powerful particle accelerator, located at the CERN laboratory near Geneva, Switzerland. In the Summer of 2012, the ATLAS and CMS experiments at CERN announced the discovery of a particle,which was later confirmed to be the Higgs boson. This was a massive accomplishment, the discovery of a particle hypothesized in 1964 that has remained elusive until now. However, this is not the end of the experimental effort. ATLAS and CMS are general purpose detectors performing a multitude of measurements, as well as carrying out many searches for Beyond the Standard Model (BSM) physics. In this dissertation, two searches are conducted for a pair-produced stop squark, the supersymmetric partner to the top quark. The stop can decay to a variety of final states, depending upon the hierarchy of the mass eigenstates formed from the linear superposition of the SUSY partners of the Higgs boson and electroweak gauge bosons. In this stop search, the relevant supersymmetric mass eigenstate is the neutralino. The searches for the stop in the 3-body decay channel presented here consist of a b-quark, W-boson, and a neutralino, with both W-bosons decaying to a lepton and a neutrino. In order to discriminate the signal from background two techniques are employed, a cut-and-count technique using recursive jigsaw variables and a technique using Boosted Decision Trees. The recursive jigsaw variables are derived using the Recursive Jigsaw Reconstruction technique, a method for decomposing measured properties event-by-event by approximating the rest frame of each intermediate particle state. These variables are powerful discriminators on their own, as shown in the cut-and-count analysis. Machine learning techniques are also utilized by training boosted decision trees, using the recursive jigsaw variables in tandem with other kinematic variables, to study whether we can enhance our discovery potential. These analyses use 139 inverse fb of 13 TeV data collected at the ATLAS experiment during Run-2 of the LHC from 2015 until 2018. No evidence of an excess beyond the SM background prediction is observed in the Recursive Jigsaw Reconstruction analysis, however, exclusion limits at 95% confidence levels are set far exceeding the previous limits. The potential for an improvement on these limits is demonstrated by training Boosted Decision Trees, a technique I hope is used in future BSM physics searches.


Physics, SUSY, Supersymmetry, high-energy, CERN, LHC, ATLAS, Hadron, Collider, Standard Model, Particle


Physical Sciences and Mathematics | Physics


Degree granted by The University of Texas at Arlington

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