论文标题
GAIA数据版本3:目录验证
Gaia Data Release 3: Catalogue Validation
论文作者
论文摘要
第三GAIA数据发布(DR3)提供了大量的新数据产品。该版本的早期部分Gaia EDR3已经为近20亿个来源提供了星体和光度数据。现在,与Gaia DR2相比,整个版本添加了改进的参数,用于径向速度,天体物理参数,可变性信息,光曲线和太阳系对象的轨道。这些改进是根据源的数量,参数信息的多样性,精度和准确性。 Gaia DR3首次提供了分光光度计和光谱的样本,并通过径向速度光谱仪,二进制恒星溶液以及候选外层次对象的表征。在发布目录之前,这些数据经历了专用的横向验证过程。本文的目的是强调此过程中发现的数据的局限性,并为目录的使用提供建议。通过对数据的统计分析,对不同产品的内部一致性的确认以及将值与外部数据或模型进行比较来获得验证。就Gaia产品的数量,多样性,精度和准确性而言,Gaia DR3是向前迈出的新一步。但是,在如此大而复杂的目录中,也发现了问题和局限性。可以在随附的数据处理论文以及性能验证论文中找到GAIA DR3释放科学质量的详细示例。在这里,我们仅关注用户应意识到科学利用数据的警告。
The third gaia data release (DR3) provides a wealth of new data products. The early part of the release, Gaia EDR3, already provided the astrometric and photometric data for nearly two billion sources. The full release now adds improved parameters compared to Gaia DR2 for radial velocities, astrophysical parameters, variability information, light curves, and orbits for Solar System objects. The improvements are in terms of the number of sources, the variety of parameter information, precision, and accuracy. For the first time, Gaia DR3 also provides a sample of spectrophotometry and spectra obtained with the Radial Velocity Spectrometer, binary star solutions, and a characterisation of extragalactic object candidates. Before the publication of the catalogue, these data have undergone a dedicated transversal validation process. The aim of this paper is to highlight limitations of the data that were found during this process and to provide recommendations for the usage of the catalogue. The validation was obtained through a statistical analysis of the data, a confirmation of the internal consistency of different products, and a comparison of the values to external data or models. Gaia DR3 is a new major step forward in terms of the number, diversity, precision, and accuracy of the Gaia products. As always in such a large and complex catalogue, however, issues and limitations have also been found. Detailed examples of the scientific quality of the Gaia DR3 release can be found in the accompanying data-processing papers as well as in the performance verification papers. Here we focus only on the caveats that the user should be aware of to scientifically exploit the data.