Theses Doctoral

Semi-Supervised Learning for Semi-Visible Jets: A Search for Dark Matter Jets at the LHC with the ATLAS Detector

Busch, Elena Laura

A search is presented for hadronic signatures of a strongly-coupled hidden dark sector, accessed via resonant production of a ?′ mediator.

The analysis uses 139 fb-1 of proton-proton collision data collected by the ATLAS experiment during Run 2 of the LHC. The ?′ mediator decays to two dark quarks, which each hadronize and decay to showers containing both dark and Standard Model particles; these showers are termed “semi-visible” jets. The final state consists of missing energy aligned with one of the jets, a topology that is ignored by most dark matter searches.

A supervised machine learning method is used to select these dark showers and reject the dominant background of mis-measured multijet events. A complementary semi-supervised anomaly detection approach introduces broad sensitivity to a variety of strongly coupled dark matter models. A resonance search is performed by fitting the transverse mass spectrum with a polynomial background estimation function.

Results are presented as limits on the effective cross section of the Z', parameterized by the fraction of invisible particles in the decay and the Z' mass. No structure in the transverse mass spectrum compatible with the signal hypothesis is observed. Z' mediator masses from ranging from 2.0 TeV to 3.5 TeV are excluded at the 95% confidence level.

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More About This Work

Academic Units
Physics
Thesis Advisors
Tuts, Philip Michael
Degree
Ph.D., Columbia University
Published Here
September 11, 2024