论文标题
高能物理中的公平AI模型
FAIR AI Models in High Energy Physics
论文作者
论文摘要
可发现的，可访问的，可互操作和可重复使用的（公平）数据原则为检查，评估和改进数据的共享方式提供了一个框架，以促进科学发现。将这些原理概括为研究软件和其他数字产品是一个活跃的研究领域。机器学习（ML）模型 - 在没有明确编程的情况下对数据进行了培训的算法 - 更普遍地是人工智能（AI）模型，是对此的重要目标，因为AI的不断增长的速度可以通过AI改变科学领域，例如实验性高能物理学（HEP）。在本文中，我们提出了HEP中AI模型的公平原理的实用定义，并描述了用于应用这些原理的模板。我们与应用于HEP的示例AI模型一起演示了模板的用途，其中使用图神经网络识别Higgs玻色子腐烂到两个底部夸克。我们报告了这种公平AI模型的鲁棒性，其跨硬件体系结构和软件框架的可移植性及其可解释性。
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning (ML) models -- algorithms that have been trained on data without being explicitly programmed -- and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template's use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.
