Online Archive of University of Virginia Scholarship
A Search for Long-Lived Particles in Signatures With Displaced Vertex Using Novel Machine Learning Techniques at CMS323 views
Author
Li, Ang, Physics - Graduate School of Arts and Sciences, University of Virginia0000-0002-4547-116X
Advisors
Neu, Chris, AS-Physics (PHYS), University of Virginia
Abstract
A search for long-lived particles produced in proton-proton collisions at a center- of-mass energy of 13TeV at the CERN LHC is presented. The search is based on data collected by the CMS experiment in 2016–2018, which corresponds to a total integrated luminosity of 137 fb−1. The search targets final states with at least one displaced vertex and missing transverse momentum. Customized vertex reconstruction and advanced machine learning algorithms are applied to increase the sensitivity of the search. Moreover, the search is designed to be model-independent to be sensitive to a wide range of new physics models. No significant excess over the background–only prediction is observed. For the mean proper decay length in the range 1–100 mm, the search excludes long-lived gluinos in the split supersymmetry model with masses below 1800 or 2000GeV, depending on the neutralino masses, at a 95% confidence level.
Li, Ang. A Search for Long-Lived Particles in Signatures With Displaced Vertex Using Novel Machine Learning Techniques at CMS. University of Virginia, Physics - Graduate School of Arts and Sciences, PHD (Doctor of Philosophy), 2023-04-27, https://doi.org/10.18130/1wnr-n456.
