Online Bayesian Optimization for Beam Alignment in the SECAR Recoil Mass Separator

TL;DR

This paper introduces an online Bayesian optimization method using Gaussian processes to efficiently and reproducibly tune ion beams in the SECAR separator, significantly outperforming manual tuning.

Contribution

First application of online Bayesian optimization with Gaussian processes for beam alignment in a nuclear physics separator system.

Findings

Achieves small angular deviations of 0-1 mrad

At least 3 times faster than manual tuning

Now routinely used for separator tuning

Abstract

The SEparator for CApture Reactions (SECAR) is a next-generation recoil separator system at the Facility for Rare Isotope Beams (FRIB) designed for the direct measurement of capture reactions on unstable nuclei in inverse kinematics. To maximize the performance of the device, careful beam alignment to the central ion optical axis needs to be achieved. This can be difficult to attain through manual tuning by human operators without potentially leaving the system in a sub-optimal and irreproducible state. In this work, we present the first development of online Bayesian optimization with a Gaussian process model to tune an ion beam through a nuclear astrophysics recoil separator. We show that the method achieves small incoming angular deviations (0-1 mrad) in an efficient and reproducible manner that is at least 3 times faster than standard hand-tuning. This method is now routinely used for all separator tuning.