论文标题
一种基于神经网络的方法，可从IR调查中选择年轻的恒星对象候选者
A neural network-based methodology to select young stellar object candidates from IR surveys
论文作者
论文摘要
观察到的年轻恒星物体（YSO）用于研究恒星形成并表征恒星形成区域。为此，YSO候选目录是根据各种调查汇编的，尤其是在红外（IR）中，并且通常使用颜色磁性图（CMD）中的简单选择方案来识别和分类YSO。我们通过使用Spitzer IR数据提出了一种通过机器学习（ML）进行YSO分类的方法。我们详细介绍了我们的方法，以确保可重复性并提供有关如何有效将ML应用于天体物理分类的深入示例。我们使用了使用四个IRAC频段（$ 3.6、4.5、5.8 $和8μm$）的Feedforward人工神经网络（ANN）和从Spitzer的$ 24 \μm$ MIPS频段将点源对象分类为CI和CII YSO YSO候选者或污染物。我们发现，通过包含数量的神经元（$ \ sim $ 25），可以有效地将ANN应用于YSO分类。在一个星形地区收集的知识已证明是在新区域预测的部分有效效率。使用多个恒星形成区域来训练网络，实现了最佳的概括能力。仔细地重新平衡培训比例是为了取得良好的成绩。我们的CI和CII YSO分别达到了90％和97％的回收率，对于我们最普遍的结果，精度高于80％和90％。我们利用ANN的灵活性为每个对象定义了每个输出类别的有效成员资格概率。发现在此概率中使用阈值可以有效地以合理的对象排除成本有效地改善分类结果。通过这种选择，我们在CI YSO上达到了90％的精度，其中一半以上。我们在Orion（365 CI，2381 CII）和NGC 2264（101 CI，469 CII）中的YSO候选人目录，我们的最终ANN在CDS上公开可用。
Observed Young Stellar Objects (YSOs) are used to study star formation and characterize star forming regions. For this purpose, YSO candidate catalogs are compiled from various surveys, especially in the infrared (IR), and simple selection schemes in colour-magnitude diagrams (CMDs) are often used to identify and classify YSOs. We propose a methodology for YSO classification through Machine Learning (ML) using Spitzer IR data. We detail our approach in order to ensure reproducibility and provide an in-depth example on how to efficiently apply ML to an astrophysical classification. We used feedforward Artificial Neural Networks (ANNs) that use the four IRAC bands ($3.6, 4.5, 5.8$ and $8 μm$) and the $24\ μm$ MIPS band from Spitzer to classify point source objects into CI and CII YSO candidates or as contaminants. We found that ANNs can efficiently be applied to YSO classification with a contained number of neurons ($\sim$ 25). Knowledge gathered on one star-forming region has shown to be partly efficient for prediction in new regions. Best generalization capacity was achieved using a combination of several star-forming regions to train the network. Carefully rebalancing the training proportions was necessary to achieve good results. We achieved above 90% and 97% recovery rate for CI and CII YSOs, respectively, with precision above 80% and 90% for our most general result. We took advantage of the ANN great flexibility to define, for each object, an effective membership probability to each output class. Using a threshold in this probability was found to efficiently improve the classification results at a reasonable cost of object exclusion. With this selection, we reached 90% precision on CI YSOs, for more than half of them. Our catalog of YSO candidates in Orion (365 CI, 2381 CII) and NGC 2264 (101 CI, 469 CII) predicted by our final ANN is publicly available at CDS.
