Relating centrality to impact parameter in nucleus-nucleus collisions

TL;DR

This paper demonstrates that, assuming Gaussian fluctuations, the impact parameter distribution in nucleus-nucleus collisions can be accurately reconstructed from experimental data without relying on specific collision models, improving centrality determination.

Contribution

It introduces a model-independent method to infer impact parameter distributions from data, assuming only Gaussian fluctuations of the observable.

Findings

Impact parameter distributions can be reconstructed up to 5% centrality.

The method applies to RHIC and LHC data.

A measure of centrality determination precision is proposed.

Abstract

In ultrarelativistic heavy-ion experiments, one estimates the centrality of a collision by using a single observable, say $n$, typically given by the transverse energy or the number of tracks observed in a dedicated detector. The correlation between $n$ and the impact parameter, $b$, of the collision is then inferred by fitting a specific model of the collision dynamics, such as the Glauber model, to experimental data. The goal of this paper is to assess precisely which information about $b$ can be extracted from data without any specific model of the collision. Under the sole assumption that the probability distribution of $n$ for a fixed $b$ is Gaussian, we show that the probability distribution of the impact parameter in a narrow centrality bin can be accurately reconstructed up to $5\%$ centrality. We apply our methodology to data from the Relativistic Heavy Ion Collider and the Large Hadron Collider. We propose a simple measure of the precision of the centrality determination, which can be used to compare different experiments.