The Annals of Applied Statistics

A frequency-calibrated Bayesian search for new particles

Shirin Golchi and Richard Lockhart

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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.

Article information

Ann. Appl. Stat., Volume 12, Number 3 (2018), 1939-1968.

Received: May 2016
Revised: January 2018
First available in Project Euclid: 11 September 2018

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Bayes rule decision set Higgs boson linear loss function sequential Monte Carlo


Golchi, Shirin; Lockhart, Richard. A frequency-calibrated Bayesian search for new particles. Ann. Appl. Stat. 12 (2018), no. 3, 1939--1968. doi:10.1214/18-AOAS1138.

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Supplemental materials

  • Supplement A: Frequency-calibrated Bayesian analysis of the on/off problem. In this supplementary file, an adaptation of the proposed approach is described for a simple signal detection problem referred to as the on/off problem that helps better understanding of the methods.