Building a predictive model of galaxy formation – I. Phenomenological model constrained to the z = 0 stellar mass function
Building a predictive model of galaxy formation – I. Phenomenological model constrained to the z = 0 stellar mass function
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建立星系形成的预测模型 – I. 受限于 z = 0 恒星质量函数的唯象模型
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发表时间：
2014
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通讯作者：
Andrew J Benson
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文献类型：
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作者：
Andrew J Benson
我们约束一个高度简化的半解析模型的星系形成使用的星系的$z\approx 0$恒星质量函数。特别注意评估的作用，随机和系统误差的恒星质量的测定，系统的不确定性模型，并在测量和建模的恒星质量函数箱之间的相关性，以构建一个现实的似然函数。我们推导出模型参数的约束条件，并探讨哪些方面的观测数据约束特定的参数组合。我们发现，我们的模型，一旦约束，提供了一个显着的匹配到恒星质量函数的测量演变为$z=1$，虽然未能显着匹配本地星系HI质量函数。几个“讨厌的参数”大大有助于模型预测的不确定性。特别是，恒星质量估计的系统误差是模型预测的不确定性的主要来源，在$z\approximat 1$，与额外的，nonaccurablible贡献所产生的系统不确定性晕质量函数和宇宙学参数的残余不确定性。忽略任何这些不确定性的来源可能会导致可行的模型被错误地排除。此外，我们证明，忽略箱之间的显着协方差观测到的恒星质量函数导致显着的偏差模型参数的约束。如果这种方法被用来检验与星系形成的物理学有关的假设，那么仔细处理约束数据和模型中的系统和随机误差是至关重要的。
We constrain a highly simplified semi-analytic model of galaxy formation using the $z\approx 0$ stellar mass function of galaxies. Particular attention is paid to assessing the role of random and systematic errors in the determination of stellar masses, to systematic uncertainties in the model, and to correlations between bins in the measured and modeled stellar mass functions, in order to construct a realistic likelihood function. We derive constraints on model parameters and explore which aspects of the observational data constrain particular parameter combinations. We find that our model, once constrained, provides a remarkable match to the measured evolution of the stellar mass function to $z=1$, although fails dramatically to match the local galaxy HI mass function. Several "nuisance parameters" contribute significantly to uncertainties in model predictions. In particular, systematic errors in stellar mass estimate are the dominant source of uncertainty in model predictions at $z\approx 1$, with additional, non-negligble contributions arising from systematic uncertainties in halo mass functions and the residual uncertainties in cosmological parameters. Ignoring any of these sources of uncertainties could lead to viable models being erroneously ruled out. Additionally, we demonstrate that ignoring the significant covariance between bins the observed stellar mass function leads to significant biases in the constraints derived on model parameters. Careful treatment of systematic and random errors in the constraining data, and in the model being constrained, are crucial if this methodology is to be used to test hypotheses relating to the physics of galaxy formation.