论文标题
Gaia数据版本3:与文献的可变对象的Gaia源交叉匹配
Gaia Data Release 3: Cross-match of Gaia sources with variable objects from the literature
论文作者
论文摘要
语境。在当前不断增加的天文调查数据量中,自动化方法至关重要。文献中已知类别的对象对于培训监督机器学习算法以及验证/验证其结果所必需。目的。这项工作的主要目标是提供与\ textit {gaia} 〜dr3源交叉匹配的文献中的全面数据集,包括大量的可变性类型和代表,以覆盖尽可能多的天空区域和与每个类相关的幅度范围。此外,从选定的调查中进行的非变量对象是针对\ textit {gaia}的可变性,并可能用作标准。该数据集可以是适用于可变性检测,分类和验证的培训集的基础。将同时采用星体(位置和适当运动)和光度法(平均幅度)(平均幅度)的Methodsa统计方法应用于选定的文献目录,以识别\ textit {Gaia}数据中已知对象的正确对应物。交叉匹配策略适用于每个目录的属性,并验证结果不包括可疑匹配项。结果。目录收集7 \,841 \,723 \ textit {gaia}源中,其中1.200万个不可变化的对象和17〜百万个星系,除了代表100〜变异性(sub)类型的4.900万可变源。结论。此数据集可满足\ textit {gaia}的可变性管道的第三个数据发布(DR3)的要求,从分类器培训到结果验证,并且预计它将成为对科学社区的有用资源,对\ textit {Gaia}数据和其他Surveys的可变性分析感兴趣。
Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for verification/validation of their results. Aims.The primary goal of this work is to provide a comprehensive data set of known variable objects from the literature cross-matched with \textit{Gaia}~DR3 sources, including a large number of both variability types and representatives, in order to cover as much as possible sky regions and magnitude ranges relevant to each class. In addition, non-variable objects from selected surveys are targeted to probe their variability in \textit{Gaia} and possible use as standards. This data set can be the base for a training set applicable in variability detection, classification, and validation. MethodsA statistical method that employed both astrometry (position and proper motion) and photometry (mean magnitude) was applied to selected literature catalogues in order to identify the correct counterparts of the known objects in the \textit{Gaia} data. The cross-match strategy was adapted to the properties of each catalogue and the verification of results excluded dubious matches. Results.Our catalogue gathers 7\,841\,723 \textit{Gaia} sources among which 1.2~million non-variable objects and 1.7~million galaxies, in addition to 4.9~million variable sources representing over 100~variability (sub)types. Conclusions.This data set served the requirements of \textit{Gaia}'s variability pipeline for its third data release (DR3), from classifier training to result validation, and it is expected to be a useful resource for the scientific community that is interested in the analysis of variability in the \textit{Gaia} data and other surveys.