A frequency-calibrated Bayesian search for new particles
Shirin Golchi, Richard Lockhart
The Annals of Applied Statistics · 2018 · Volume 12 · Issue 3
Abstract
The statistical procedure used in the search for new particles is investigated in this paper. The discovery of the Higgs particles is used to lay out the problem and the existing procedures. A Bayesian hierarchical model is proposed to address inference about the parameters of interest while incorporating uncertainty about the nuisance parameters into the model. In addition to inference, a decision making procedure is proposed. A loss function is introduced that mimics the important features of a discovery problem. Given the importance of controlling the “false discovery” and “missed detection” error rates in discovering new phenomena, the proposed procedure is calibrated to control for these error rates.
Keywords
Bayes rule, decision set, Higgs boson, linear loss function, sequential Monte Carlo
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