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arXiv 1407.5151astro-ph.GA
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不确定红移的光度学方法估算星系的光度和恒星质量

Estimating Luminosities and Stellar Masses of Galaxies Photometrically without Determining Redshifts

B. C. Hsieh, H. K. C. Yee

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17 Pages, 7 figures, accepted for publication in ApJ
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摘要：

大型直接成像巡天通常使用模板拟合技术来估计星系的光度红移，然后应用于获得重要的星系属性，如光度和恒星质量。这些估计可能是嘈杂的，并遭受系统的偏见，因为可能的错误选择的模板和传播的光度红移的不确定性。我们介绍了一种算法，直接经验光度法（DEmP），它可以用来直接估计这些数量使用训练集，绕过光度红移的测定。DEmP还采用了两种技术，以尽量减少所产生的影响，从一个通量有限的样本的训练集星系红移的非均匀分布。首先，对于每个输入星系，使用具有最接近输入星系的光度和颜色的训练集星系的子集进行拟合。第二，训练集被人工重新采样以产生红移或其他属性的平坦分布，例如，发光度为了测试DEmP的性能，我们使用了一个4滤波器带模拟目录，以检查其恢复红移，光度，恒星质量，光度和恒星质量函数的能力。我们还比较了结果，从两个公开的模板拟合方法，发现DEmP算法优于两者。我们发现，如果训练集具有均匀的红移分布，不仅在光度红移方面，而且在估计光度和恒星质量方面都产生最好的结果。DEmP方法在估算诸如近红外光度和恒星质量等数量时特别强大，只使用少量光学波段的数据。

英文摘要：

Large direct-imaging surveys usually use a template-fitting technique to estimate photometric redshifts for galaxies, which are then applied to derive important galaxy properties such as luminosities and stellar masses. These estimates can be noisy and suffer from systematic biases because of the possible mis-selection of templates and the propagation of the photometric redshift uncertainty. We introduce an algorithm, the Direct Empirical Photometric method (DEmP), which can be used to directly estimate these quantities using training sets, bypassing photometric redshift determination. DEmP also applies two techniques to minimize the effects arising from the non-uniform distribution of training-set galaxy redshifts from a flux-limited sample. First, for each input galaxy, fitting is performed using a subset of the training-set galaxies with photometry and colors closest to those of the input galaxy. Second, the training set is artificially resampled to produce a flat distribution in redshift, or other properties, e.g., luminosity. To test the performance of DEmP, we use a 4-filter-band mock catalog to examine its ability to recover redshift, luminosity, stellar mass, and luminosity and stellar-mass functions. We also compare the results to those from two publicly available template-fitting methods, finding that the DEmP algorithm outperforms both. We find resampling the training set to have a uniform redshift distribution produces the best results not only in photometric redshift, but also in estimating luminosity and stellar mass. The DEmP method is especially powerful in estimating quantities such as near-IR luminosities and stellar mass using only data from a small number of optical bands.

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