论文标题

银河系聚类和星系 - 半镜头镜头的一致且同时建模,并具有次荷兰的丰度匹配

Consistent and simultaneous modelling of galaxy clustering and galaxy-galaxy lensing with Subhalo Abundance Matching

论文作者

Contreras, Sergio, Angulo, Raul E., Chaves-Montero, Jonás, White, Simon D. M., Aricò, Giovanni

论文摘要

星系的空间分布及其重力镜头信号提供了星系形成物理和宇宙学的互补测试。但是,只有当两个探针都是准确,一致地建模的,才能完全利用它们的协同作用。在本文中,我们证明可以使用subhalo丰度匹配(称为羞耻感)的扩展来实现这一目标。具体而言,我们使用由TNG300流体动力模拟构建的模拟目录表明,羞耻可以同时建模红移空间星系相关功能和星系 - 果胶镜头的多孔,而不会在SDSS体积的统计上无明显的偏见,并在SDSS体积的统计上偏见,并在SDSS体积的不确定性中进行尺度r = [0.6--30] MPC/H H. H.使用Baryonification方案对星系 - 果胶镜头中的重型过程进行建模,可以将羞耻的有效性范围扩展到r = [0.1-30] MPC/H。值得注意的是,我们的模型只有五个自由参数才能达到这一级别的精度,而不是描述Baryonification模型的参数。在固定的宇宙学上,我们发现Galaxy-galaxy镜头提供了一般的一致性测试,但几乎没有关于Galaxy建模参数以外的其他信息。但是,如果只能提供投影相关函数,它确实会改善约束,就像仅具有光度红移的调查中。我们期望羞耻在更广泛的尺度上具有更高的保真度,而不是诸如光环职业分布建模之类的传统方法。因此,它应该为分析下一代大规模调查提供一个更强大,更强大的工具。

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Subhalo Abundance Matching, dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift-space galaxy correlation function and galaxy-galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r = [0.6-30] Mpc/h. Modelling the baryonic processes in galaxy-galaxy lensing with a baryonification scheme allows SHAMe's range of validity to be extended to r = [0.1-30] Mpc/h. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy-galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys.

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