开源框架助力粒子加速器控制自动化与算法对比
Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls
- 提供标准化接口统一加速器优化算法
- 内置工具函数加速开发，支持多算法快速迁移
- 适合加速器实验室研究人员与工程师使用
Geoff 是一组 Python 工具包，构成粒子加速器控制自动化的通用框架。全球加速器实验室正探索机器学习技术以提升加速器性能与运行时间，涌现出大量方法与算法。Geoff 的目标是整合这些方法，减少在不同算法间比较或迁移的摩擦。它提供优化问题的标准化接口、加速开发的实用函数，并附带一个集成所有功能的参考图形界面应用。Geoff 是由欧洲核子中心（CERN）开发的开源库，由 CERN 与 GSI 在 EURO-LABS 项目框架下协作维护和更新。本文概述了 Geoff 的设计、功能及当前应用情况。
原文摘要 · Abstract (English)
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.
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