mlsanityData to 14 Oct 2026

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

arXiv 2506.03796 ↗First posted 4 Jun 2025physics.acc-ph cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 52 references that are in the map. First posted on arXiv 4 Jun 2025.

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References · 3

  1. 2020pymoo: Multi-objective Optimization in Pythoncited by 200
  2. 2018Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solverscited by 9
  3. 2018Escaping local minima with derivative-free methods: a numerical investigationcited by 4

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