标题： 结合天体物理数据集与CRUMB 显示英文标题

标题： Combining astrophysical datasets with CRUMB

摘要： 目前，天文机器学习领域缺乏广泛使用的基准数据集；大多数研究采用的是自定义数据集，这些数据集通常未公开发布，使得模型之间的比较变得困难。 本文介绍了一个公开可用的图像数据集 CRUMB（Constructing Reliable Unified Multiwavelength Benchmarks）。CRUMB 是从文献中存在的四个“母”数据集构建而成的 Fanaroff-Riley 星系图像数据集。 除了提供这些星系最大的图像数据集外，CRUMB 还采用了两级标签系统：“基本”标签用于分类，“完整”标签提供了四个母数据集中原始的类别标签，允许保留不同数据集对同一图像类别判断的分歧，并可选择性地访问来自任意母数据集组合的源数据。 显示英文摘要

摘要： At present, the field of astronomical machine learning lacks widely-used benchmarking datasets; most research employs custom-made datasets which are often not publicly released, making comparisons between models difficult. In this paper we present CRUMB, a publicly-available image dataset of Fanaroff-Riley galaxies constructed from four "parent" datasets extant in the literature. In addition to providing the largest image dataset of these galaxies, CRUMB uses a two-tier labelling system: a "basic" label for classification and a "complete" label which provides the original class labels used in the four parent datasets, allowing for disagreements in an image's class between different datasets to be preserved and selective access to sources from any desired combination of the parent datasets.