Priors for New Physics

TL;DR

This paper introduces a Bayesian method for constructing multi-parameter priors in physics models, using reference analysis to improve inference in complex models like supersymmetry.

Contribution

It proposes a novel prior construction method based on reference analysis, enabling more objective Bayesian inference for multi-parameter physics models.

Findings

Applied to supersymmetric models, demonstrating practical utility.

Produced consistent posterior distributions for model parameters.

Enhanced the interpretability of Bayesian analysis in physics contexts.

Abstract

The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our idea is to initiate the chain of inference with the reference prior for a likelihood function that depends on a single parameter of interest that is a function of the parameters of the physics model. The reference posterior density of the parameter of interest induces on the parameter space of the physics model a class of posterior densities. We propose to continue the chain of inference with a particular density from this class, namely, the one for which indistinguishable models are equiprobable and use it as the prior for subsequent analysis. We illustrate our method by applying it to the constrained minimal supersymmetric Standard Model and two non-universal variants of it.