We introduce a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.

翻译：我们从基础物理学研究 -- -- 包括粒子物理学、粒子粒子物理学、粒子粒子物理学、以及光子物理学和核物理学 -- -- 中开始收集数据集,供监督的机器学习研究使用。这些数据集包含超时顶层天体、宇宙射线诱发空气淋浴、黄物质阶段转变和发电机级历史,这些数据集被公诸于众,以简化今后在基础物理学中跨学科机器学习和转移学习方面的工作。根据这些数据,我们提出了一个简单而灵活的、以图表为基础的神经网络结构,可以很容易地应用于这些领域广泛的监督学习任务。我们表明,我们的方法接近于所有数据集的最先进的专门方法。为了简化对各种问题的适应,我们提供了易于了解的指示,说明如何建造与基本物理学相关的数据结构的图表,并为其中的若干数据提供代码执行。我们提出的方法和所有参考算法也得到了实施。