Angular variation of hard back-to-back hadron suppression in heavy-ion collisions

TL;DR

This paper investigates how the suppression of back-to-back hadron correlations varies with angle and impact parameter in heavy-ion collisions, providing insights into medium density and pathlength effects using hydrodynamical and Monte Carlo models.

Contribution

It introduces a systematic study of angular variation of back-to-back hadron suppression at different impact parameters using combined hydrodynamical and Monte Carlo models.

Findings

Suppression varies with angle relative to the reaction plane.

Model successfully reproduces suppression patterns in central collisions.

Provides a framework to constrain energy loss and medium evolution models.

Abstract

The basic idea of jet tomography is to infer information about the density evolution of the medium created in heavy-ion (A-A) collisions by studying the suppression of hard probes in an A-A environment as compared to the baseline process known from p-p collisions. The suppression of back-to-back correlations in heavy-ion collisions allows, due to a different geometrical bias, a view into the medium which is qualitatively different from the one offered by single hadron suppression. A control parameter for the suppression corresponding to a systematic variation of in-medium pathlengths and density can be obtained by studying collisions at finite impact parameter b. A systematic variation of pathlength can then be introduced by studying the suppression pattern as a function of the angle phi with the reaction plane. Using a 3-d hydrodynamical evolution model for the medium and a Monte-Carlo model which has been shown to successfully reproduce the measured suppression of back-to-back correlations in central collisions of Au-Au at 200 AGeV, we compute the suppression as a function of phi for b of 2.4 fm. 4.5 fm, 6.3 fm and 7.5 fm. Given that this involves variations in both control parameters b and phi a comparison with data should eventually allow to place strong constraints on the combination of energy loss model and medium evolution model.