Holographic complex potential of a quarkonium from deep learning

TL;DR

This paper employs deep learning to determine the complex potential of static quarkonium, revealing how temperature affects its stability and dissociation in quark-gluon plasma, thus advancing high-energy physics understanding.

Contribution

It introduces a novel deep learning approach to compute the complex potential of quarkonium, linking emergent metrics to physical properties and temperature effects.

Findings

Dissociation length decreases with temperature.

Imaginary potential magnitude increases with temperature.

Deep learning effectively models quarkonium behavior in plasma.

Abstract

Utilizing an emergent metric developed from deep learning techniques, we determine the complex potential associated with static quarkonium. This study explores the disintegration process of quarkonium by analyzing the real component of this potential, which is crucial for understanding its stability in various conditions. We show that the dissociation length, the critical distance at which a quark and antiquark pair disintegrate, decreases as the temperature increases. Furthermore, our assessment of the imaginary component of the potential indicates an increase in the magnitude of the imaginary potential for quarkonium as temperatures rise. This enhancement contributes to the quarkonium's suppression within the quark-gluon plasma, mirroring the anticipated outcomes from QCD. Our findings not only confirm the theoretical predictions but also demonstrate the efficacy of deep learning methods in advancing our understanding of high-energy particle physics.