论文标题
从休息框的光度函数到观察者框架颜色分布:应对宇宙学模拟中的下一个挑战
From rest-frame luminosity functions to observer-frame colour distributions: tackling the next challenge in cosmological simulations
论文作者
论文摘要
由于形成了星系的辐射输出的大量和复杂的混合物,因此在模拟中繁殖的最具挑战性目的的信息数量是最具挑战性但内容的最具挑战性的信息。随着越来越多的调查利用宽带颜色作为其目标选择标准的一部分,在模拟中生产现实的SED对于协助调查设计和观察的解释是必要的。使用最先进的半分析模型\鲨鱼和SED Generator \ Prospect的最新成功繁殖观察到的光度函数(LF)从Far-UV到Far-Ir的成功代表了迈向更好的星系颜色预测的关键步骤。我们表明,使用\鲨鱼\和\ Prospect \我们可以密切复制Panchromation Gama调查中观察到的光学色分布。反馈,恒星形成,中央 - 卫星相互作用以及通过尘埃重新处理的辐射对此成就至关重要。前三个过程产生了双峰分布,而灰尘衰减定义了蓝色和红色种群的位置和形状。虽然观察和模拟之间的天真比较显示出已知的卫星星系过度淬灭的问题,但从GAMA中使用的同一组发现者进行了经验动机的观察误差和分类,从而大大降低了这种张力。 $ \ sim 15 \%的中心/卫星作为卫星/中心分类的随机重新分配与组发现器的结果非常相似,从而提供了一种比较模拟与观察值的计算方法。
Galaxy spectral energy distributions (SEDs) remain among the most challenging yet informative quantities to reproduce in simulations due to the large and complex mixture of physical processes that shape the radiation output of a galaxy. With the increasing number of surveys utilising broadband colours as part of their target selection criteria, the production of realistic SEDs in simulations is necessary for assisting in survey design and interpretation of observations. The recent success in reproducing the observed luminosity functions (LF) from far-UV to far-IR, using the state-of-the-art semi-analytic model \shark\ and the SED generator \prospect, represents a critical step towards better galaxy colour predictions. We show that with \shark\ and \prospect\ we can closely reproduce the optical colour distributions observed in the panchromatic GAMA survey. The treatment of feedback, star formation, central-satellite interactions and radiation re-processing by dust are critical for this achievement. The first three processes create a bimodal distribution, while dust attenuation defines the location and shape of the blue and red populations. While a naive comparison between observation and simulations displays the known issue of over-quenching of satellite galaxies, the introduction of empirically-motivated observational errors and classification from the same group finder used in GAMA greatly reduces this tension. The introduction of random re-assignment of $\sim 15\%$ of centrals/satellites as satellites/centrals on the simulation classification closely resembles the outcome of the group finder, providing a computationally less intensive method to compare simulations with observations.