Gaia EDR3 Catalogs of Machine-Learned Radial Velocities

<strong>Gaia EDR3 Catalogs of Machine-Learned Radial Velocities</strong> Spatially complete Test-Set and Machine-Learned Radial Velocity (ML-RV) Catalogs described in Dropulic et al., arXiv:2205.12278. The spatially complete Test-Set Catalog contains a total of 4,332,657 stars, while the spatially complete ML-RV Catalog contains 91,840,346 stars. We provide Gaia EDR3 Source IDs, the network-predicted line-of-sight velocity in km/s, and the network-predicted uncertainty in km/s. We have included a simple Jupyter notebook demonstrating how to import the data, and make a simple histogram with it. If you find this catalog useful in your work, please cite Dropulic et al. arXiv:2205.12278, as well as Dropulic et al. ApJL 915, L14 (2021) arXiv:2103.14039.**机器学习径向速度盖亚EDR3星表集** 本数据集包含空间完备测试集星表与机器学习径向速度（Machine-Learned Radial Velocity，简称ML-RV）星表，相关研究细节详见Dropulic等人发表于arXiv:2205.12278的论文。其中空间完备测试集星表共收录4,332,657颗恒星，空间完备ML-RV星表则包含91,840,346颗恒星。本数据集提供盖亚EDR3源标识符、神经网络预测的径向速度（单位：千米每秒）以及神经网络预测的速度不确定度（单位：千米每秒）。我们附带了一份简易Jupyter交互式笔记本，演示如何导入数据集并利用其绘制简单直方图。若您在研究工作中使用本星表，请引用Dropulic等人发表于arXiv:2205.12278的论文，同时引用Dropulic等人发表于《天体物理学杂志快报》（ApJL）915卷L14期（2021年）、arXiv:2103.14039的研究成果。