2024 Theses Doctoral
Semi-Supervised Learning for Semi-Visible Jets: A Search for Dark Matter Jets at the LHC with the ATLAS Detector
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.
Subjects
Files
- Busch_columbia_0054D_18757.pdf application/pdf 10.7 MB Download File
More About This Work
- Academic Units
- Physics
- Thesis Advisors
- Tuts, Philip Michael
- Degree
- Ph.D., Columbia University
- Published Here
- September 11, 2024