二维实验分布的极大似然方法及其在星系合并分数中的应用
A maximum likelihood method for bidimensional experimental distributions, and its application to the galaxy merger fraction
摘要：
利用自动形态指数和光度红移确定场星系的星系合并分数时，如果观测误差处理不当，会受到多种偏差的影响。在这里，我们使用极大似然技术纠正这些偏差。该方法考虑了观测误差的影响，从统计上恢复了星系在红移不对称空间中二维分布的真实形状，这是推断星系合并分数红移演化所需要的。我们测试的方法与合成目录，并显示其适用性限制。该方法的准确性取决于星表特征，如源的数量或实验误差大小。我们表明，即使当合并是这样的，bin大小接近的观测误差的大小，极大似然方法恢复星系的红移和不对称空间的真实分布。我们提供了一个循序渐进的指南，应用极大似然技术来恢复任何一个或二维分布的观测误差。
英文摘要：
The determination of galaxy merger fraction of field galaxies using automatic morphological indices and photometric redshifts is affected by several biases if observational errors are not properly treated. Here, we correct these biases using maximum likelihood techniques. The method takes into account the observational errors to statistically recover the real shape of the bidimensional distribution of galaxies in redshift - asymmetry space, needed to infer the redshift evolution of galaxy merger fraction. We test the method with synthetic catalogs and show its applicability limits. The accuracy of the method depends on catalog characteristics such as the number of sources or the experimental error sizes. We show that the maximum likelihood method recovers the real distribution of galaxies in redshift and asymmetry space even when binning is such that bin sizes approach the size of the observational errors. We provide a step-by-step guide to applying maximum likelihood techniques to recover any one- or bidimensional distribution subject to observational errors.
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