Dynamic Exclusion of Low-Fidelity Data in Bayesian Optimization for Autonomous Beamline Alignment
This work addresses a domain-specific problem for scientists at synchrotron light sources by providing incremental improvements in beam quality and optimization efficiency.
The study tackled the problem of faulty data impairing Bayesian Optimization for beamline alignment by developing methods to identify and exclude low-fidelity data points, resulting in successful classification and faster convergence to optimal beam quality.
Aligning beamlines at synchrotron light sources is a high-dimensional, expensive-to-sample optimization problem, as beams are focused using a series of dynamic optical components. Bayesian Optimization is an efficient machine learning approach to finding global optima of beam quality, but the model can easily be impaired by faulty data points caused by the beam going off the edge of the sensor or by background noise. This study, conducted at the National Synchrotron Light Source II (NSLS-II) facility at Brookhaven National Laboratory (BNL), is an investigation of methods to identify untrustworthy readings of beam quality and discourage the optimization model from seeking out points likely to yield low-fidelity beams. The approaches explored include dynamic pruning using loss analysis of size and position models and a lengthscale-based genetic algorithm to determine which points to include in the model for optimal fit. Each method successfully classified high and low fidelity points. This research advances BNL's mission to tackle our nation's energy challenges by providing scientists at all beamlines with access to higher quality beams, and faster convergence to these optima for their experiments.