Model discrimination in pseudoscalar-meson photoproduction

TL;DR

This paper introduces a geometric measure to quantify the difference between models in pseudoscalar-meson photoproduction and proposes a method to evaluate how experimental data can discriminate among these models.

Contribution

It presents a novel geometric approach to quantify model differences and a method to assess the discriminative power of experimental measurements in amplitude space.

Findings

A new geometric measure for model distance in amplitude space

A method to evaluate probability distributions from measurements

Guidelines for additional measurements needed for model discrimination

Abstract

To learn about a physical system of interest, experimental results must be able to discriminate among models. We introduce a geometrical measure to quantify the distance between models for pseudoscalar-meson photoproduction in amplitude space. Experimental observables, with finite accuracy, map to probability distributions in amplitude space, and the characteristic width scale of such distributions needs to be smaller than the distance between models if the observable data are going to be useful. We therefore also introduce a method for evaluating probability distributions in amplitude space that arise as a result of one or more measurements, and show how one can use this to determine what further measurements are going to be necessary to be able to discriminate among models.