BOOSTR

BOOSTR数据集由费米国家加速器实验室创建，旨在提供费米实验室加速器控制系统的周期性时间序列数据。该数据集包含55个与加速器高精度调节直接相关的设备读数和设置，主要用于支持人工智能在先进控制系统中的应用，如强化学习和自主异常检测。数据集的创建过程涉及从加速器控制网络中收集数据，并通过时间对齐处理确保数据准确性。BOOSTR数据集的应用领域主要集中在加速器网络的调优和控制，支持机器学习方法的开发和验证。

The BOOSTR dataset was developed by Fermi National Accelerator Laboratory, with the objective of providing periodic time-series data for the laboratory’s accelerator control system. This dataset contains 55 device readings and settings directly related to the high-precision regulation of the accelerator, and is primarily intended to support the application of artificial intelligence in advanced control systems, such as reinforcement learning and autonomous anomaly detection. The dataset creation workflow involves collecting data from the accelerator control network and conducting time-alignment processing to guarantee data accuracy. The application scope of the BOOSTR dataset is primarily focused on the tuning and control of accelerator networks, facilitating the development and validation of machine learning methods.