Speaker
Description
The BAMline, a beamline for material science research at BESSY II, has been operated by the Bundesanstalt für Materialforschung und -prüfung for over two decades. In the last few years, Bayesian optimization (BO) with Gaussian processes (GP) has been introduced as a transformative method in this setting. This contribution highlights the integration and impact of BO and GP in refining BAMline operations.
We will explore the impact of integrating Bayesian methods, especially when combined with Gaussian processes, on the operational efficiency of the BAMline. Our discussion commences with an overview of the fundamental concepts of active learning and optimization. This is followed by an in-depth analysis of specific case studies, drawing on our direct experience with these innovative methods.
For this our focus is on three key applications:
- Alignment of Optical Elements (DCM and DMM): The implementation of
Bayesian optimization using Gaussian processes has revolutionized the
alignment process for the Double Crystal Monochromator (DCM) and the
Double Multilayer Monochromator (DMM). This approach greatly reduced
manual effort and enhanced the efficiency and effectiveness of the
alignment procedure. - Optimal Spot Selection for Spatial Mapping in XRF Studies: In X-ray
fluorescence (XRF) experiments, determining the most informative
sampling spots is crucial. The introduction of BO, informed by GP,
has significantly improved the selection process, especially in large
or heterogeneous samples. This methodology ensures maximum
information gain, optimizing the balance between comprehensive
mapping and resource management. - Selection of Optimal Energies for XANES Measurements in GEXANES
Geometry: In the specific context of X-ray Absorption Near Edge
Structure (XANES) studies using Grazing Exit (GEXANES) geometry, the
Bayesian approach with Gaussian processes is now utilized to identify
the most effective energy settings. This results in detailed spectral
data acquired in a much shorter time.
We also address challenges and limitations encountered during implementation. Additionally, the versatility of this approach in addressing a range of different research questions will be demonstrated.
