来自Kilo-Degree Survey Data Release 3的类星体目录
Catalog of quasars from the Kilo-Degree Survey Data Release 3
摘要：
我们提出了一个目录的类星体从宽波段光度ugri数据的千度调查数据发布3（KiDS DR 3）。QSO由随机森林（RF）监督机器学习模型识别，在SDSS DR 14光谱数据上训练。我们首先清除输入KiDS数据中具有过度噪声，缺失或其他问题测量的条目。应用特征重要性分析，然后我们调整算法，并在KiDS多波段目录中识别出17个最有用的分类特征，即星等，颜色，星等比和恒星度指数。我们使用t-SNE算法将多维光度数据映射到2D平面上，并比较训练集和推理集的覆盖率。我们将推断集限制为r<22，以避免外推超出训练覆盖的特征空间，因为SDSS光谱样本比KiDS浅得多。这在最终的推断样本中给出了340万个物体，随机森林从中识别出190，000个类星体候选者。97%的准确度，纯度为91%，和87%的完整性，来自从SDSS提取的测试集，而不是在训练中使用，确认与外部光谱和光度类星体目录重叠的KiDS足迹的比较。我们的结果的鲁棒性加强了类星体候选者在r波段的数量计数，以及他们的中红外颜色可从WISE。对我们在盖亚DR 2中发现的类星体候选者的松弛和自行的分析表明，p（类星体）>0.8的概率切割对于纯度是最佳的，而p（类星体）>0.7对于更好的完整性是优选的。我们的研究提出了第一个全面的类星体选择从深高质量的KiDS数据，并将作为通用的研究的基础上检测到的类星体人口的调查。
英文摘要：
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.
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