Evolutionary Algorithm Optimization of Zeeman Deceleration: Is It Worthwhile for Longer Decelerators?

Abstract

In Zeeman deceleration, only a small subset of low-field-seeking particles in the incoming beam possess initial velocities and positions that place them within the phase-space acceptance of the device. In order to maximize the number of particles that are successfully decelerated to a selected final velocity, we seek to optimize the phase-space acceptance of the decelerator. Three-dimensional particle trajectory simulations are employed to investigate the potential benefits of using a covariance matrix adaptation evolutionary strategy (CMA-ES) optimization method for decelerators longer than 12 stages and for decelerating species other than H atoms. In all scenarios considered, the evolutionary algorithm-optimized sequences yield vastly more particles within the target velocity range. This is particularly evident in scenarios where standard sequences are known to perform poorly; simulations show that CMA-ES optimization of a standard sequence decelerating H atoms from an initial velocity of 500 ms–1 down to a final velocity of 200 ms–1 in a 24-stage decelerator produces a considerable 5921% (or 60-fold) increase in the number of successfully decelerated particles. Particle losses that occur with standard pulse sequences—for example, arising from the coupling of longitudinal and transverse motion—are overcome in the CMA-ES optimization process as the passage of all particles through the decelerator is explicitly considered and focusing effects are accounted for in the optimization process.

Applying machine learning optimization methods to the production of a quantum gas

“…Recently, machine learning techniques have been applied in the field of ultracold quantum matter to optimize individual laser cooling [5,17] and evaporative cooling [4,18] stages, achieving significant improvements in the performance of these apparatuses. The optimizations in each case were performed on a subset of the atomic cooling processes [19,20], and did not consider the changeover between each process, which increases the likelihood that the entire cooling sequence will become trapped in a local optimum.…”

Quantum-State Control and Manipulation of Paramagnetic Molecules with Magnetic Fields

“…Unconventional switching sequences have come up with a range of ways to overcome the constraints imposed by the properties of the current pulses applied to the coils. These include the application of a reversed current pulse to certain solenoids and evolutionary algorithm optimization of the current switching sequence (53)(54)(55)(56)(57)(58). These methods have successfully optimized the density of low-velocity atoms produced by a Zeeman decelerator, but they have yet to be experimentally adopted for the deceleration of a molecular paramagnetic species.…”

A stand-alone magnetic guide for producing tuneable radical beams

“…A number of different Zeeman decelerator designs and switching sequences have been implemented, and a range of radical species have been successfully decelerated. [24][25][26][27][28][29][30][31][32][33][34] However, filtering elements must be added to a Zeeman decelerator to remove all of the nondecelerated species travelling along the main beam axis before the apparatus can be interfaced with a stationary target, such as an ion trap.…”