Multiparameter optimisation of a magneto-optical trap using deep learning

TL;DR

This paper demonstrates how deep learning can optimize magneto-optical trapping of atoms, discovering solutions that outperform traditional methods and potentially leading to new insights into atomic cooling dynamics.

Contribution

It introduces a deep neural network approach to optimize atomic cooling and trapping, surpassing existing solutions and offering a novel method for complex system optimization.

Findings

Deep learning solutions outperform traditional adiabatic methods.

Optimized solutions achieve higher optical densities.

Potential for new understanding of atomic cooling dynamics.

Abstract

Many important physical processes have dynamics that are too complex to completely model analytically. Optimisation of such processes often relies on intuition, trial-and-error, or the construction of empirical models. Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. We implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions vastly outperform best known solutions producing higher optical densities. This may provide a pathway to a new understanding of the dynamics of the cooling and trapping processes in cold atomic ensembles.