Learning holographic horizons

TL;DR

This paper uses machine learning to predict holographic entropies from simple time series features, revealing how basic boundary data can encode bulk geometric information in holographic duality.

Contribution

It demonstrates that simple features of pressure anisotropy time series can accurately predict apparent and event horizon areas in holographic models.

Findings

Accurate horizon area predictions from pressure anisotropy features

Simple boundary data can encode bulk geometric information

Entropy functions relate to information measures from one-point functions

Abstract

We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas. We show that simple features of only the time series of the pressure anisotropy, namely the values and half-widths of the maxima and minima, the times these are attained, and the times of the first zeroes can predict the areas of the apparent and event horizons in the dual bulk geometry at all times with a fixed maximum length ($10$) of the input vector. We also argue that the entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations.