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Title: Neutral Supersymmetric Higgs Boson Searches

Thesis/Dissertation ·
DOI:https://doi.org/10.2172/968354· OSTI ID:968354
 [1]
  1. Imperial College, London (United Kingdom)

In some Supersymmetric extensions of the Standard Model, including the Minimal Supersymmetric Standard Model (MSSM), the coupling of Higgs bosons to b-quarks is enhanced. This enhancement makes the associated production of the Higgs with b-quarks an interesting search channel for the Higgs and Supersymmetry at D0. The identification of b-quarks, both online and offline, is essential to this search effort. This thesis describes the author's involvement in the development of both types of b-tagging and in the application of these techniques to the MSSM Higgs search. Work was carried out on the Level-3 trigger b-tagging algorithms. The impact parameter (IP) b-tagger was retuned and the effects of increased instantaneous luminosity on the tagger were studied. An extension of the IP-tagger to use the z-tracking information was developed. A new b-tagger using secondary vertices was developed and commissioned. A tool was developed to allow the use of large multi-run samples for trigger studies involving b-quarks. Offline, a neural network (NN) b-tagger was trained combining the existing offline lifetime based b-tagging tools. The efficiency and fake rate of the NN b-tagger were measured in data and MC. This b-tagger was internally reviewed and certified by the Collaboration and now provides the official b-tagging for all analyses using the Run IIa dataset at D0. A search was performed for neutral MSSM Higgs bosons decaying to a b{bar b} pair and produced in association with one or more b-quarks. Limits are set on the cross-section times the branching ratio for such a process. The limits were interpreted in various MSSM scenarios. This analysis uses the NN b-tagger and was the first to use this tool. The analysis also relies on triggers using the Level-3 IP b-tagging tool described previously. A likelihood discriminant was used to improve the analysis and a neural network was developed to cross-check this technique. The result of the analysis has been submitted to PRL and is comparable to the result from CDF in the same channel which uses approximately twice the integrated luminosity.

Research Organization:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC02-07CH11359
OSTI ID:
968354
Report Number(s):
FERMILAB-THESIS-2009-40; TRN: US0904640
Country of Publication:
United States
Language:
English