Extracting the temperature dependence in high-$p_\perp$ particle energy loss

TL;DR

This paper introduces a new observable based on $R_{AA}$ to measure the temperature dependence of high-$p_\perp$ particle energy loss in QGP, providing a decisive test for theoretical models and challenging the existing $L^2 T^3$ paradigm.

Contribution

It proposes a novel observable to extract the temperature dependence exponent of parton energy loss, enabling more stringent tests of theoretical models against experimental data.

Findings

The observable can effectively test the $\Delta E/E \propto L^2 T^3$ paradigm.

Combining analytical and numerical methods, the study shows potential for precise temperature dependence extraction.

Experimental data with reduced errors can distinguish between different energy loss mechanisms.

Abstract

The suppression of high-$p_\perp$ particles is one of the main signatures of parton energy loss during its passing through the QGP medium, and is reasonably reproduced by different theoretical models. However, a decisive test of the reliability of a certain energy loss mechanism, apart from its path-length, is its temperature dependence. Despite its importance and comprehensive dedicated studies, this issue is still awaiting for more stringent constraints. To this end, we here propose a novel observable to extract temperature dependence exponent of high-$p_{\perp}$ particle's energy loss, based on $R_{AA}$. More importantly, by combining analytical arguments, full-fledged numerical calculations and comparison with experimental data, we argue that this observable is highly suited for testing (and rejecting) the long-standing $\Delta E/E \propto L^2 T^3$ paradigm. The anticipated significant reduction of experimental errors will allow direct extraction of temperature dependence, by considering different centrality pair in $A + A$ collisions (irrespective of the nucleus size) in high-$p_\perp$ region. Overall, our results imply that this observable, which reflects the underlying energy loss mechanism, is very important to distinguish between different theoretical models.