COSMOS场中极红目标的分类
Classification of Extremely Red Objects in the COSMOS Field
摘要：
我们提出了一个z ~1极红天体（ERO）的分类研究，使用HST/ACS，斯皮策/IRAC，和COSMOS领域的地基图像的组合。我们的样本包括大约5300个ERO，i-Ks>2.45（AB，相当于Vega中的I-Ks=4）和Ks<=21.1（AB）。对于我们样本中的ERO，我们使用ACS F814 W图像计算它们的浓度，不对称性，以及它们的基尼系数和最亮20%光的二阶矩。利用这些形态学参数和Spitzer/IRAC [3.6]-[8.0]颜色，光谱能量分布（SED）拟合方法，我们将ERO分为两类：老星系（OG）和年轻的尘埃星暴星系（DG）。我们发现，在我们的样本中，OG和DG的比例是相似的，我们的样本中约48%的ERO是OG，其中52%是DG。为了减少这三种不同的分类方法的冗余，我们进行了主成分分析的测量的ERO，并发现形态参数和SED是有效的分离OG和DG。[3.6]-[8.0]颜色取决于红化、红移和光度精度，很难区分星爆和椭圆之间的区别线周围的ERO。我们调查的依赖性的分数ERO对他们的观测性能，结果表明，DG变得越来越重要，在较暗的幅度，红颜色，和更高的红移。
英文摘要：
We present a study of the classification of z ~1 extremely red objects (EROs), using a combination of HST/ACS, Spitzer/IRAC, and ground-based images of the COSMOS field. Our sample includes about 5300 EROs with i-Ks>2.45 (AB, equivalently I-Ks=4 in Vega) and Ks<=21.1 (AB). For EROs in our sample, we compute, using the ACS F814W images, their concentration, asymmetry, as well as their Gini coefficient and the second moment of the brightest 20% of their light. Using those morphology parameters and the Spitzer/IRAC [3.6]-[8.0] color, the spectral energy distribution (SED) fitting method, we classify EROs into two classes: old galaxies (OGs) and young, dusty starburst galaxies (DGs). We found that the fraction of OGs and DGs in our sample is similar, about 48 percentages of EROs in our sample are OGs, and 52 percentages of them are DGs. To reduce the redundancy of these three different classification methods, we performed a principal component analysis on the measurements of EROs, and find that morphology parameters and SEDs are efficient in segregating OGs and DGs. The [3.6]-[8.0] color, which depends on reddening, redshift, and photometric accuracy, is difficult to separate EROs around the discriminating line between starburst and elliptical. We investigate the dependence of the fraction of EROs on their observational properties, and the results suggest that DGs become increasingly important at fainter magnitudes, redder colors, and higher redshifts.
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