Numerical optimization of quantum vacuum signals

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 put forward Bayesian optimization as a new and powerful means to optimize photonic quantum vacuum signals. We demonstrate its great potential on the example of the well-studied case of two-beam collisions. Apart from providing an ideal benchmark case, this immediately gives new physics results. Namely, Bayesian optimization 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. Published by the American Physical Society 2024