Numerical optimization of quantum vacuum signals

TL;DR

This paper demonstrates how Bayesian optimization can effectively enhance quantum vacuum signals in high-intensity laser experiments by optimizing beam parameters and identifying key physical processes.

Contribution

It introduces Bayesian optimization as a tool for maximizing quantum vacuum signals and applies it to two-beam collision scenarios, including elliptical beam waists and harmonic focusing.

Findings

Optimal beam waist sizes identified for maximum signals

Physical processes leading to detectable signals clarified

Bayesian optimization proves effective in high-dimensional parameter spaces

Abstract

The identification of prospective scenarios for observing quantum vacuum signals in high-intensity laser experiments requires both accurate theoretical predictions and the exploration of high-dimensional parameter spaces. Numerical simulations address the first requirement, while optimization provides an efficient solution for the second one. In the present work, we demonstrate the potential of Bayesian optimization in maximizing photonic quantum vacuum signals on the example of two-beam collisions. This allows us to find the optimal waist sizes for beams with elliptic cross sections, and to identify the specific physical process leading to a discernible signal in a coherent harmonic focusing configuration scenario.