  Analysis and interpretation of spectrum and correlation data from high-energy
nuclear collisions is currently controversial because two opposing physics
narratives derive contradictory implications from the same data---one narrative
claiming collision dynamics is dominated by dijet production and
projectile-nucleon fragmentation, the other claiming collision dynamics is
dominated by a dense, flowing QCD medium. Opposing interpretations seem to be
supported by alternative data models, and current model-comparison schemes are
unable to distinguish between them. There is clearly need for a convincing new
methodology to break the deadlock. In this study we introduce Bayesian
Inference (BI) methods applied to angular correlation data as a basis to
evaluate competing data models. For simplicity the data considered are
projections of 2D angular correlations onto 1D azimuth from three centrality
classes of 200 GeV AuAu collisions. We consider several data models typical of
current model choices, including Fourier series (FS) and a Gaussian plus
various combinations of individual cosine components. We evaluate model
performance with BI methods and with power-spectrum (PS) analysis. We find that
the FS-only model is rejected in all cases by Bayesian analysis which always
prefers a Gaussian. A cylindrical quadrupole cos(2\phi) is required in some
cases but rejected for 0-5%-central AuAu collisions. Given a Gaussian centered
at the azimuth origin "higher harmonics" cos(m\phi) for m > 2 are rejected. A
model consisting of Gaussian + dipole cos(\phi) + quadrupole cos(2\phi)
provides good 1D data descriptions in all cases.
