Date of Award

Spring 2005

Document Type


Degree Name

Doctor of Philosophy (PhD)


Computational Analysis and Modeling

First Advisor

Neeti Parashar


The current data-taking phase of the DØ detector at Fermilab, called Run II, is designed to aid the search for the Higgs Boson. The neutral Higgs is postulated to have a mass of 117 GeV. One of the channels promising the presence of this hypothetical particle is through the decay of b-quark into a muon. The process of identifying a b-quark in a jet using muon as a reference is b-tagging with a muon tag.

At the current data taking and analysis rate, it will take long to reach the process of identifying valid events. The triggering mechanism of the experiment, consisting of 3 levels of combined hardware, firmware and software writes final physics events at the rate of 50 Hz to data disks, with Level-3 alone accounting for the reduction from 1 kHz to 50 Hz. This large rejection is achieved through algorithms implemented in the search for key physics processes.

The work presented in this dissertation is the development of a fast b-tagging algorithm using central-matched muons, called L3FBTagMU. Additional tools such as the impact parameter tracks and calorimeter jets have been used to tag B jets. The dR or the differential increment in cone radius is the most significant variable introduced. Plots within thresholds of dR for both Z → bb Monte Carlo and monitor stream data show similar efficiency trends when checked against other parameters.

The differential efficiencies saturate at dR within 0.5 to 0.7 range. Differential bins of 0.1 intervals project an overall efficiency of tagging a b-jet in any event is 17.25 in data. This is in good agreement with the theory.

The algorithm is currently running online and of line through the DØ database repository. This work is primarily used by the b-id, B-Physics and Higgs Physics groups for their physics analysis wherein the above b-tagging efficiency serves as a crucial tool. The prospect for optimizing the physics potential using this algorithm is very promising for current and future analyses.