Strom, DavidKilgallon, Aaron2022-10-262022-10-262022-10-26https://hdl.handle.net/1794/27743A search for emerging jets in the dijet topology is presented here using 139 ifb of √s = 13 TeV Run 2 ATLAS proton-proton collision data. Emerging jets constitute a class of dark jet models that have long-lived hadronization components, resulting in unique signatures within particle detectors. These jet signatures are the result of phenomenological considerations of self-interacting dark matter. These models provide an explanation for the baryon-antibaryon asymmetry as well as a well-motivated dark matter candidate particle, which make them particularly compelling. Due to the unusual nature of these jets containing high displaced track and displaced vertex multiplicities that vary significantly on the dark sector parameters, machine learning techniques such as unsupervised classification are ideal in the search for these types of models. A Classification Without Labels method known as the CWoLa method was used to extract limits on heavy Beyond the Standard Model vector boson Z’ particles that produce pairs of emerging jets in the large-R dijet topology. Limits were set on the cross-sections of these signatures and exclude Z’ particles decaying to emerging jets from 10 fb to 2 fb between masses of 1.3 TeV and 4.0 TeV. Production of Z’ particles with masses up to 3.1 TeV and a fixed 20 GeV width were excluded for dark sector couplings down to 0.015. Future considerations for emerging jets analyses are shown in the context of dedicated emerging jets triggers that were designed for use at ATLAS in Run 3.en-USAll Rights Reserved.dijet analysisemerging jet triggersemerging jetsunsupervised learningElectronic Thesis or Dissertation