A Bayesian Search for the Higgs Particle
Shirin Golchi, Richard Lockhart
TL;DR
This paper introduces a Bayesian hierarchical model and decision-making procedure for Higgs boson searches, integrating theory and data analysis to improve statistical inference and error control.
Contribution
It presents a novel Bayesian framework that unifies discovery and exclusion steps in Higgs searches, enhancing statistical rigor and calibration.
Findings
Bayesian hierarchical model effectively incorporates theoretical information.
Unified decision procedure improves error rate control.
Method demonstrates potential for more accurate Higgs boson detection.
Abstract
The statistical procedure used in the search for the Higgs boson is investigated in this paper. A Bayesian hierarchical model is proposed that uses the information provided by the theory in the analysis of the data generated by the particle detectors. In addition, we develop a Bayesian decision making procedure that combines the two steps of the current method (discovery and exclusion) into one and can be calibrated to satisfy frequency theory error rate requirements. .
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Gaussian Processes and Bayesian Inference · Distributed Sensor Networks and Detection Algorithms