Nonflow suppression in flow analysis with a maximum likelihood estimator

TL;DR

This paper demonstrates that maximum likelihood estimation effectively mitigates non-flow effects in flow analysis, offering an alternative to traditional methods by modeling non-flow contributions and improving flow harmonic extraction.

Contribution

The paper introduces the use of maximum likelihood estimator for flow analysis, specifically addressing non-flow effects with toy models and comparing its performance to standard methods.

Findings

MLE provides accurate flow harmonic estimates in toy models.

Performance improves with known likelihood forms in non-flow scenarios.

Method effectively handles detector acceptance issues.

Abstract

We show that the maximum likelihood estimator (MLE) is an effective tool for mitigating non-flow effects in flow analysis. To this end, one constructs two toy models that simulate non-flow contributions corresponding to particle decay and momentum conservation, respectively. The performance of MLE is analyzed by comparing it against standard approaches such as particle correlation and event plane methods. For both cases, MLE is observed to provide a reasonable estimate of the underlying flow harmonics, and in particular, its performance can be further improved when the specific form of the likelihood in the presence of non-flow can be assessed. The dependencies of extracted flow harmonics on the multiplicity of individual events and the total number of events are analyzed. Additionally, it is shown that the proposed approach performs efficiently in addressing deficiencies in detector acceptance. These findings suggest MLE as a compelling alternative to standard methods for flow analysis.