Identifying weak critical fluctuations of intermittency in heavy-ion collisions with topological machine learning

TL;DR

This paper introduces a topological machine learning approach using neural networks to detect weak critical fluctuations in heavy-ion collision data, improving the identification of signals related to the QCD critical point amidst background noise.

Contribution

The study applies topological machine learning to classify weak critical fluctuation signals, enhancing detection capabilities in heavy-ion collision experiments.

Findings

Successfully classified weak signal events from background noise.

Accurately determined the intermittency index for weak signals.

Demonstrated effectiveness of topological features in signal detection.

Abstract

Large density fluctuations of conserved charges have been proposed as a promising signature for exploring the QCD critical point in heavy-ion collisions. These fluctuations are expected to exhibit a fractal or scale-invariant behavior, which can be probed by intermittency analysis. Recent high-energy experimental studies reveal that the signal of critical fluctuations related to intermittency is very weak and thus could be easily obscured by the overwhelming background particles in the data sample. Employing a point cloud neural network with topological machine learning, we can successfully classify weak signal events from background noise by the extracted distinct topological features, and accurately determine the intermittency index for weak signal event samples.