Multivariate discrimination and the Higgs + W/Z search
Kevin Black, Jason Gallicchio, John Huth, Michael Kagan, Matthew D., Schwartz, Brock Tweedie

TL;DR
This paper develops a systematic multivariate discriminant optimization method, introduces the Significance Improvement Characteristic (SIC) as a visualization tool, and demonstrates its application to Higgs boson searches at colliders, achieving significant sensitivity improvements.
Contribution
The paper introduces the SIC method for optimizing multivariate discriminants and applies it to Higgs searches, exploring new variables and demonstrating potential sensitivity gains.
Findings
SIC curves reveal numerical instabilities and convergence behavior.
Combining multiple variables improves discrimination power.
Potential 10-20% improvement in significance over existing methods.
Abstract
A systematic method for optimizing multivariate discriminants is developed and applied to the important example of a light Higgs boson search at the Tevatron and the LHC. The Significance Improvement Characteristic (SIC), defined as the signal efficiency of a cut or multivariate discriminant divided by the square root of the background efficiency, is shown to be an extremely powerful visualization tool. SIC curves demonstrate numerical instabilities in the multivariate discriminants, show convergence as the number of variables is increased, and display the sensitivity to the optimal cut values. For our application, we concentrate on Higgs boson production in association with a W or Z boson with H -> bb and compare to the irreducible standard model background, Z/W + bb. We explore thousands of experimentally motivated, physically motivated, and unmotivated single variable discriminants.…
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