SuperNova加速度探针（SNAP）：研究光度红移优化
SuperNova Acceleration Probe (SNAP): Investigating Photometric Redshift Optimization
摘要：
本文的目的是研究如何优化的精度测光红移的SNAP一样的任务。我们重点讨论测光红移的精度如何依赖于星等极限和信噪比、波长覆盖范围、滤波器的数量和形状以及观测到的星系类型。我们使用模拟的星系目录构建重现观测到的星系光度函数从GOODS，并获得光度红移使用模板拟合方法。通过使用一个类似于真实数据的星表，我们可以估计出预期的星系数密度，从而可以推导出光度红移。我们发现测光红移的精度强烈依赖于信噪比（S/N）（即，需要S/N>10才能得到精确的光度红移）。光度红移的准确性也依赖于星系类型，早期类型星系的散射较小。比较使用不同滤波器组的结果，我们发现包括U带对于减少离群值的分数是重要的，即，“灾难性的失败”。在积分时间相同的情况下，与较窄的滤波器（分辨率>~5）相比，使用分辨率~ 4的宽重叠滤波器可以得到更好的光度红移。我们发现，平方响应曲线的过滤器会导致散射略高，主要是由于在微弱的幅度较高的比例离群值。我们还比较了9个滤镜组和17个滤镜组，其中我们假设后一组中每个滤镜的可用曝光时间是第一组的一半。我们发现，9过滤器组给出了更准确的红移为更多的对象，并达到更高的红移，而17过滤器组是在明亮的幅度提供更好的结果。
英文摘要：
The aim of this paper is to investigate ways to optimize the accuracy of photometric redshifts for a SNAP like mission. We focus on how the accuracy of the photometric redshifts depends on the magnitude limit and signal-to-noise ratio, wave-length coverage, number of filters and their shapes and observed galaxy type. We use simulated galaxy catalogs constructed to reproduce observed galaxy luminosity functions from GOODS, and derive photometric redshifts using a template fitting method. By using a catalog that resembles real data, we can estimate the expected number density of galaxies for which photometric redshifts can be derived. We find that the accuracy of photometric redshifts is strongly dependent on the signal-to-noise (S/N) (i.e., S/N>10 is needed for accurate photometric redshifts). The accuracy of the photometric redshifts is also dependent on galaxy type, with smaller scatter for earlier type galaxies. Comparing results using different filter sets, we find that including the U-band is important for decreasing the fraction of outliers, i.e., ``catastrophic failures''. Using broad overlapping filters with resolution ~4gives better photometric redshifts compared to narrower filters (resolution >~5) with the same integration time. We find that filters with square response curves result in a slightly higher scatter, mainly due to a higher fraction of outliers at faint magnitudes. We also compare a 9-filter set to a 17-filter set, where we assume that the available exposure time per filter in the latter set is half that of the first set. We find that the 9-filter set gives more accurate redshifts for a larger number of objects and reaches higher redshift, while the 17-filter set is gives better results at bright magnitudes.
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