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In the context of data modeling and comparisons between different fit models,Bayesian analysis calls that model best which has the largest evidence, theprior-weighted integral over model parameters of the likelihood function.Evidence calculations automatically take into account both the usualchi-squared measure and an Occam factor which quantifies the price for addingextra parameters. Applying Bayesian analysis to projections onto azimuth of 2Dangular correlations from 200 GeV AuAu collisions, we consider typical modelchoices including Fourier series and a Gaussian plus combinations of individualcosine components. We find that models including a Gaussian component areconsistently preferred over pure Fourier-series parametrizations, sometimesstrongly so. For 0-5% central collisions the Gaussian-plus-dipole modelperforms better than Fourier Series models or any other combination ofGaussian-plus-multipoles.

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