A versatile framework for attitude tuning of beamlines at advanced light sources

TL;DR

This paper introduces a flexible, AI-compatible framework for automating attitude tuning in beamline experiments at light sources, enhancing efficiency and user interaction through versatile interfaces and simulation tools.

Contribution

A novel Mamba-based framework supporting diverse evaluation functions, optimisation algorithms, and AI integration for attitude tuning in light source beamlines.

Findings

Successfully tuned a polycapillary lens and X-ray spectrometer

Demonstrated framework's versatility with CLI and GUI interfaces

Enabled specialized tuning with custom algorithms

Abstract

Aside from regular beamline experiments at light sources, the preparation steps before these experiments are also worth systematic consideration in terms of automation; a representative category in these steps is attitude tuning, which typically appears in names like beam focusing, sample alignment etc. With the goal of saving time and manpower in both writing and using in mind, a Mamba-based attitude-tuning framework is created. It supports flexible input/output ports, easy integration of diverse evaluation functions, and free selection of optimisation algorithms; with the help from Mamba's infrastructure, machine learning (ML) and artificial intelligence (AI) technologies can also be readily integrated. The tuning of a polycapillary lens and of an X-ray emission spectrometer are given as examples for the general use of this framework, featuring powerful command-line interfaces (CLIs) and friendly graphical user interfaces (GUIs) that allow comfortable human-in-the-loop control. The tuning of a Raman spectrometer demonstrates more specialised use of the framework with customised optimisation algorithms. With similar applications in mind, our framework is estimated to be capable of fulfilling a majority of attitude-tuning needs. Also reported is a virtual-beamline mechanism based on easily customisable simulated detectors and motors, which facilitates both testing for developers and training for users.