Maximizing the capture velocity of molecular magneto-optical traps with Bayesian optimization

TL;DR

This paper introduces a Bayesian optimization method to enhance the capture velocity of molecular magneto-optical traps, demonstrated on calcium fluoride, potentially improving trapping of heavy molecules like BaH and BaF.

Contribution

The paper presents a general Bayesian optimization approach for molecular MOTs, specifically optimizing capture velocity, which is a novel application in this context.

Findings

Achieved capture velocities over 20 m/s for CaF using optimized parameters.

Capture velocity depends logarithmically on laser beam power within 25-400 mW.

Method could enable more robust trapping of heavy molecules like BaH and BaF.

Abstract

Magneto-optical trapping (MOT) is a key technique on the route towards ultracold molecular ensembles. However, the realization and optimization of magneto-optical traps with their wide parameter space is particularly difficult. Here, we present a very general method for the optimization of molecular magneto-optical trap operation by means of Bayesian optimization (BO). As an example for a possible application, we consider the optimization of a calcium fluoride (CaF) MOT for maximum capture velocity. We find that both the $X^2\Sigma^+\,$ to $A^2\Pi_{1/2}\,$ and the $X^2\Sigma^+\,$ to $B^2\Sigma^+\,$ transition to allow for capture velocities larger than $20$ m/s with $24$ m/s and $23$ m/s respectively at a total laser power of $200$ mW. In our simulation, the optimized capture velocity depends logarithmically on the beam power within the simulated power range of $25$ to $400$ mW. Applied to heavy molecules such as BaH and BaF with their low capture velocity MOTs it might offer a route to far more robust magneto-optical trapping.