Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls
It addresses the need for standardized tools in particle accelerator laboratories to improve performance and uptime through automation, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper introduces Geoff, a Python framework for automating particle accelerator controls, aiming to harmonize diverse machine learning approaches and reduce friction in comparing or migrating between them, with development led by CERN and GSI as part of the EURO-LABS project.
Geoff is a collection of Python packages that form a framework for automation of particle accelerator controls. With particle accelerator laboratories around the world researching machine learning techniques to improve accelerator performance and uptime, a multitude of approaches and algorithms have emerged. The purpose of Geoff is to harmonize these approaches and to minimize friction when comparing or migrating between them. It provides standardized interfaces for optimization problems, utility functions to speed up development, and a reference GUI application that ties everything together. Geoff is an open-source library developed at CERN and maintained and updated in collaboration between CERN and GSI as part of the EURO-LABS project. This paper gives an overview over Geoff's design, features, and current usage.