Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls

TL;DR

Geoff is an open-source Python framework designed to streamline and standardize the automation and optimization of particle accelerator controls, facilitating machine learning research and performance improvements.

Contribution

It introduces a unified framework with standardized interfaces and tools to compare and migrate various optimization algorithms in particle accelerator control systems.

Findings

Provides a flexible, standardized interface for optimization problems

Includes utility functions to accelerate development

Features a GUI for integrated control and monitoring

Abstract

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.