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arXiv 1507.03016astro-ph.GAastro-ph.IM
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用颜色测量方法估算恒星质量的不确定性

On the Uncertainties of Stellar Mass Estimates via Colour Measurements

Joel C. Roediger, Stephane Courteau

备注：
Accepted to MNRAS. 43 pages, 12 figures, 3 tables
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摘要：

质量-光-色关系（MLCR），来自恒星星族合成模型，被广泛用于估计星系恒星质量（M $_*$），但其固有的偏见和局限性的详细调查仍然缺乏。我们量化的几个潜在的不确定性来源，使用光学和近红外（NIR）光度测定的代表性样本附近的星系从室女星系团。我们的方法相结合的多波段测光MLCR产生强大的恒星质量，而在M $_*$的错误减少，同时考虑更多的波段。先前的假设在一个人的恒星星族建模占主导地位的错误预算，创建一个颜色依赖的偏差高达0.6德克斯，如果使用近红外通量（0.3德克斯，否则）。这与光谱能量分布（SED）拟合方法相关的系统误差相匹配，表明MLCR不会受到太多额外偏差的影响。此外，MLCR和SED拟合产生类似程度的随机误差（$\sim$ 0.1-0.14 dex）时，适用于模拟星系，平均而言，相当于真正的星系与M $_* \sim$ 10 $^{8-11}$ M $_{\odot}$的质量。积分光度法的使用在M $_*$测量中引入了额外的不确定性，其水平为0.05-0.07 dex。我们认为，使用MLCR，而不是耗时的SED适合，是合理的情况下，复杂的模型参数空间（涉及，例如，多参数恒星形成的历史）和/或大型数据集。测量M $_*$的空间分辨方法应适用于小样本量和/或要求精度小于0.1 dex的情况。附录提供了$grizH$滤波器组的十种颜色排列的MLCR转换。

英文摘要：

Mass-to-light versus colour relations (MLCRs), derived from stellar population synthesis models, are widely used to estimate galaxy stellar masses (M$_*$) yet a detailed investigation of their inherent biases and limitations is still lacking. We quantify several potential sources of uncertainty, using optical and near-infrared (NIR) photometry for a representative sample of nearby galaxies from the Virgo cluster. Our method for combining multi-band photometry with MLCRs yields robust stellar masses, while errors in M$_*$ decrease as more bands are simultaneously considered. The prior assumptions in one's stellar population modelling dominate the error budget, creating a colour-dependent bias of up to 0.6 dex if NIR fluxes are used (0.3 dex otherwise). This matches the systematic errors associated with the method of spectral energy distribution (SED) fitting, indicating that MLCRs do not suffer from much additional bias. Moreover, MLCRs and SED fitting yield similar degrees of random error ($\sim$0.1-0.14 dex) when applied to mock galaxies and, on average, equivalent masses for real galaxies with M$_* \sim$ 10$^{8-11}$ M$_{\odot}$. The use of integrated photometry introduces additional uncertainty in M$_*$ measurements, at the level of 0.05-0.07 dex. We argue that using MLCRs, instead of time-consuming SED fits, is justified in cases with complex model parameter spaces (involving, for instance, multi-parameter star formation histories) and/or for large datasets. Spatially-resolved methods for measuring M$_*$ should be applied for small sample sizes and/or when accuracies less than 0.1 dex are required. An Appendix provides our MLCR transformations for ten colour permutations of the $grizH$ filter set.

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