A naive Bayes classifier for identifying Class II YSOs
A naive Bayes classifier for identifying Class II YSOs
复制标题
用于识别 II 类 YSO 的朴素贝叶斯分类器
DOI：
10.1093/mnras/stad301
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发表时间：
2023
影响因子：
4.8
通讯作者：
Wilson A
中科院分区：
文献类型：
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作者：
Wilson A
构建了一种用于识别II类yso的朴素贝叶斯分类器，并将其应用于北银面包含800万个具有良好视差的gaaedr3源的区域。该分类器使用了5个特征：Gaia g波段变化率、wismid -infrared excess、UKIDSS和2MASS近红外excess、IGAPS h - α excess和相对于主序列的超亮度。通过选择适合手头任务的后验阈值，平衡完整性和纯度的竞争需求，获得候选二类yso列表。在阈值后验大于0.5时，我们的分类器识别6504个候选II类yso。在这个阈值下，我们发现识别第II类YSOs的假阳性率约为0.02%，真阳性率约为87%。ROC曲线迅速上升到接近1，曲线下面积在0.998左右或更好，表明分类器在识别候选II类YSOs方面是有效的。我们的候选地图显示了三个以前未被发现的集群或关联。当将我们的结果与其他年轻恒星分类器发表的目录进行比较时，我们发现每个分类器中有四分之一到四分之三的高概率候选者是唯一的，这告诉我们没有一个分类器可以找到所有的年轻恒星。
A naive Bayes classifier for identifying Class II YSOs has been constructed and applied to a region of the Northern Galactic Plane containing 8 million sources with good qualityGaiaEDR3 parallaxes. The classifier uses the five features:Gaia G-band variability,WISEmid-infrared excess, UKIDSS and 2MASS near-infrared excess, IGAPS Hα excess, and overluminosity with respect to the main sequence. A list of candidate Class II YSOs is obtained by choosing a posterior threshold appropriate to the task at hand, balancing the competing demands of completeness and purity. At a threshold posterior greater than 0.5, our classifier identifies 6504 candidate Class II YSOs. At this threshold, we find a false positive rate around 0.02 per cent and a true positive rate of approximately 87 per cent for identifying Class II YSOs. The ROC curve rises rapidly to almost one with an area under the curve around 0.998 or better, indicating the classifier is efficient at identifying candidate Class II YSOs. Our map of these candidates shows what are potentially three previously undiscovered clusters or associations. When comparing our results to published catalogues from other young star classifiers, we find between one quarter and three quarters of high probability candidates are unique to each classifier, telling us no single classifier is finding all young stars.