Star-galaxy separation in the AKARI NEP deep field
Star-galaxy separation in the AKARI NEP deep field
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DOI：
10.1051/0004-6361/201118108
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发表时间：
2012-03
影响因子：
6.5
通讯作者：
A. Solarz;A. Solarz;A. Pollo;A. Pollo;T. Takeuchi;A. Pȩpiak;H. Matsuhara;T. Wada;S. Oyabu;T. Takagi;T. Goto;Y. Ohyama;C. Pearson;C. Pearson;C. Pearson;H. Hanami;T. Ishigaki
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作者：
A. Solarz;A. Solarz;A. Pollo;A. Pollo;T. Takeuchi;A. Pȩpiak;H. Matsuhara;T. Wada;S. Oyabu;T. Takagi;T. Goto;Y. Ohyama;C. Pearson;C. Pearson;C. Pearson;H. Hanami;T. Ishigaki
背景。开发一种用于对在红外波段深度巡天中探测到的天体进行分类的方法至关重要。我们特别需要一种仅利用红外信息将星系与恒星区分开来的方法，以便研究星系的性质，例如估计角关联函数，且不引入任何额外偏差。
目的。我们的目标是在从2到24μm的9个AKARI/IRC波段收集的AKARI北黄道极（NEP）深度巡天数据中区分恒星和星系，这些波段覆盖近红外和中红外波长（以下分别简称NIR和MIR）。我们计划在未来的研究中根据我们的标准从一个样本中估计NIR和MIR星系的关联函数。
方法。我们使用支持向量机（SVM）来研究AKARI多色空间中恒星和星系的分布。我们通过计算它们的红外恒星性参数（sgc）来定义这些天体的训练样本。我们创建了最有效的分类器，然后在整个样本上进行测试。我们通过斯巴鲁望远镜获得的辅助光学数据以及创建欧几里得归一化数密度图来确认所开发的区分方法。
结果。利用红外SVM分类器，我们在红外多色空间中确定星系的准确率达到90%，确定恒星的准确率达到98%。源计数以及与光学数据的比较（在选择恒星时一致性为65%，在选择星系时为96%）证实我们的恒星/星系区分方法是可靠的。
结论。基于红外sgc选择的训练样本，用SVM方法得出的红外分类器在没有任何先前目标天体选择的情况下，在红外波段深度巡天中选择恒星和星系时被证明是非常高效和准确的。
Context. It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI north ecliptic pole (NEP) deep survey collected in nine AKARI/IRC bands from 2 to 24 μm that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods. We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results. We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions. The infrared classifier derived with the SVM method based on infrared sgc – selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.