Optimal modeling of 1D azimuth correlations in the context of Bayesian inference
Michiel B. De Kock, Hans C. Eggers, Thomas A. Trainor

TL;DR
This paper applies Bayesian inference to 1D azimuth correlation data from high-energy nuclear collisions, evaluating different models to resolve conflicting interpretations about collision dynamics.
Contribution
The study introduces Bayesian inference methods to analyze azimuth correlation data, providing a rigorous way to distinguish between competing physical models.
Findings
Bayesian analysis rejects Fourier series-only models in favor of Gaussian-based models.
A Gaussian plus dipole and quadrupole model fits data well across centrality classes.
Higher harmonics beyond m=2 are generally rejected by the data.
Abstract
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…
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