The Centrality Dependence of the Parton Bubble Model for high energy heavy ion collisions and fireball surface substructure at RHIC

In an earlier paper we developed a QCD inspired theoretical parton bubble model (PBM) for RHIC/LHC. The PBM quantitatively agreed with the strong charged particle pair correlations observed by the STAR collaboration at RHIC in the highest energy Au + Au central collisions, and also agreed with the Hanbury Brown and Twiss (HBT) observed small final state source size approximately 2f radii in the transverse momentum range above 0.8 GeV/c. The model assumed a substructure of a ring of localized adjoining 2f radius bubbles(gluonic hot spots) perpendicular to the collider beam direction, centered on the beam, at mid-rapidity and located on the expanding fireball surface of the Au + Au collisions. In this paper we extend the model (PBME) to include the changing development of bubbles with centrality from the most central region where bubbles are very important to the most peripheral where the bubbles are gone. Energy density is found to be related to bubble formation and as centrality decreases the maximum energy density and bubbles shift from symmetry around the beam axis to the reaction plane region causing a strong correlation of bubble formation with elliptic flow. We obtained reasonably quantitative agreement (within a few percent of the total correlations) with a new precision RHIC experiment which extended the centrality region investigated to the range 0-80% (most central to most peripheral). The characteristics and behavior of the bubbles imply they represent a significant substructure formed on the surface of the fireball at kinetic freezeoutComment: ACCEPTED for publication in Phys. Rev. C. minor referee changes.20 pages, 12 figures, 3 table

Local Variation as a Statistical Hypothesis Test

The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm