Bayesian model comparison for one-dimensional azimuthal correlations in 200GeV AuAu collisions

TL;DR

This paper applies Bayesian analysis to select the best model for azimuthal correlations in 200 GeV AuAu collisions, favoring models with Gaussian components over pure Fourier series, especially in central collisions.

Contribution

It introduces Bayesian evidence as a criterion for model comparison in azimuthal correlation analysis, highlighting the superiority of Gaussian-inclusive models over Fourier-only models.

Findings

Gaussian-plus-dipole model outperforms Fourier series in central collisions

Bayesian evidence favors models with Gaussian components

Models with extra parameters are penalized unless they significantly improve fit

Abstract

In the context of data modeling and comparisons between different fit models, Bayesian analysis calls that model best which has the largest evidence, the prior-weighted integral over model parameters of the likelihood function. Evidence calculations automatically take into account both the usual chi-squared measure and an Occam factor which quantifies the price for adding extra parameters. Applying Bayesian analysis to projections onto azimuth of 2D angular correlations from 200 GeV AuAu collisions, we consider typical model choices including Fourier series and a Gaussian plus combinations of individual cosine components. We find that models including a Gaussian component are consistently preferred over pure Fourier-series parametrizations, sometimes strongly so. For 0-5% central collisions the Gaussian-plus-dipole model performs better than Fourier Series models or any other combination of Gaussian-plus-multipoles.