One of the primary goals of present and future collider experiments is to discover the mechanism responsible for electroweak symmetry breaking, the simplest model being the standard model Higgs mechanism. We have shown [1] that the discovery reach for the Higgs boson at the Fermilab Tevatron in future collider runs can be significantly improved by using neural networks in data analysis. Here we present Bayesian and neural network methods to measure the mass of the Higgs boson with better precision than in conventional analyses.
[1] P. C. Bhat, R. Gilmartin and H. B. Prosper, e-print:hep-ph/0001152 (to appear in Phys. Rev. D).

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