偏置关系、环境依赖性和恒星形成星系生长速率的估算
Biasing relation, environmental dependencies and estimation of the growth rate from star forming galaxies
摘要：
星系恒星形成率（SFR）和暗物质（DM）之间的联系是至关重要的宇宙学信息的提取下一代光谱调查，将目标发射线恒星形成星系。使用从各种半解析模型（SAM）获得的公开可用的模拟星系目录，我们探索了SFR-DM与光速方法（Feix et al. 2016）的关系，用于从光度/SFR偏移推断增长率$f$。着重讨论了在10 s-100 s Mpc尺度上SFR分布对环境密度的依赖性。我们表明，从光的速度的方法的应用程序的欧几里德一样的调查是不受环境影响的偏见。在所有模型中，测量的$β=f/b$参数的精密度为$z=1$时的$σ_β< 0.17$。这转化为$σ_f \sim 0.22$和$σ_{(fσ_8)}\sim 0.1$的误差，而不需要对质量功率谱进行假设。这些误差与最近对星系团簇中红移空间扭曲的分析大致相同。与以前的研究一致，偏置因子，$b$大致是一个规模独立的，恒星形成星系的SFR的常数函数。其在$z=1$的值范围为$1.2$至$1.5$，具体取决于SAM配方。虽然在所有SAMs密集的环境中，拥有更高恒星质量的星系，但SFR对环境的依赖性更大。在大多数模型中，SFR概率分布在较密集的区域中偏向于较大的值。一个模型表现出相反的趋势，即高SFR在密集环境中被抑制。
英文摘要：
The connection between galaxy star formation rate (SFR) and dark matter (DM) is of paramount importance for the extraction of cosmological information from next generation spectroscopic surveys that will target emission line star forming galaxies. Using publicly available mock galaxy catalogs obtained from various semi-analytic models (SAMs) we explore the SFR-DM connection in relation to the speed-from-light method (Feix et al. 2016) for inferring the growth rate, $f$, from luminosity/SFR shifts. Emphasis is given to the dependence of the SFR distribution on the environment density on scales of 10s-100s Mpc. We show that the application of the speed-from-light method to an Euclid-like survey is not biased by environmental effects. In all models, the precision on the measured $β=f/b$ parameter is $σ_β< 0.17$ at $z=1$. This translates into errors of $σ_f \sim 0.22$ and $σ_{(fσ_8)}\sim 0.1$, without invoking assumptions on the mass power spectrum. These errors are in the same ballpark as recent analyses of the redshift space distortions in galaxy clustering. In agreement with previous studies, the bias factor, $b$ is roughly a scale-independent, constant function of the SFR for star forming galaxies. Its value at $z=1$ ranges from $1.2$ to $1.5$ depending on the SAM recipe. Although in all SAMs denser environments host galaxies with higher stellar masses, the dependence of the SFR on the environment is more involved. In most models the SFR probability distribution is skewed to larger values in denser regions. One model exhibits an inverted trend where high SFR is suppressed in dense environment.
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