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arXiv 2103.14199astro-ph.GAastro-ph.IM
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基于梯度提升决策树的CLAUDS + HSC-SSP恒星、星系和AGN分类

Classifying Stars, Galaxies and AGN in CLAUDS+HSC-SSP Using Gradient Boosted Decision Trees

Anneya Golob, Marcin Sawicki, Andy D. Goulding, Jean Coupon

摘要:

将星表中的天体分类为恒星、星系或活动星系核是任何星系统计研究的关键部分。我们描述了我们的管道为非常深的CLAUDS+ HSC-SSP $u^*grizy$数据集开发的二元(恒星/星系)和多类(恒星/星系/I型AGN/II型AGN)分类。我们的方法使用XGBoost实现的梯度提升树(GBT)的训练合奏的模型,采取光度,颜色,最大表面亮度,有效半径从所有可用的波段作为输入,并输出的概率,对象属于每个类正在考虑。在$i_{AB}<25$,我们的双星/星系模型具有AUC=0.9974,并且在使我们的样本的加权F1得分最大化的阈值处,选择具有99.7%纯度和99.8%完整性的星系样本。我们测试模型的能力,概括到对象比在训练过程中看到的暗,并发现外推的1-2个星等是合理的,为大多数应用程序提供的训练样本中的星系是代表的范围内的红移和颜色的星系在目标样本。我们还进行了探索性分析的方法的能力,以确定活动星系核使用一个小的X射线选择的样品,并发现它持有的承诺,分类I型活动星系核,虽然它执行不太好II型活动星系核。我们的研究结果表明,GBT提供了一个灵活的,强大的和有效的方法进行分类的目录对象在大型天文成像调查。

英文摘要:

Classifying catalog objects as stars, galaxies, or AGN is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed for the very deep CLAUDS+HSC-SSP $u^*grizy$ dataset. Our method uses the XGBoost implementation of Gradient Boosted Trees (GBT) to train ensembles of models which take photometry, colours, maximum surface brightnesses, and effective radii from all available bands as input, and output the probability that an object belongs to each of the classes under consideration. At $i_{AB}<25$ our binary star/galaxy model has AUC=0.9974 and at the threshold that maximizes our sample's weighted F1 score, selects a sample of galaxies with 99.7% purity and 99.8% completeness. We test the model's ability to generalize to objects fainter than those seen during training and find that extrapolation of ~1-2 magnitudes is reasonable for most applications provided that the galaxies in the training sample are representative of the range of redshifts and colours of the galaxies in the target sample. We also perform an exploratory analysis of the method's ability to identify AGN using a small x-ray selected sample and find that it holds promise for classifying type I AGN, although it performs less well for type II AGN. Our results demonstrate that GBTs provide a flexible, robust and efficient method for performing classification of catalog objects in large astronomical imaging surveys.

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