4.7 Article

Galaxy assembly bias: a significant source of systematic error in the galaxy-halo relationship

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stu1383

关键词

galaxies: evolution; galaxies: haloes; cosmology: theory; dark matter; large-scale structure of Universe

资金

  1. US National Science Foundation [AST 1108802]
  2. University of Pittsburgh
  3. National Science Foundation [PHYS-1066293]
  4. US Department of Energy [DE-AC02-07CH11359]
  5. Division Of Astronomical Sciences
  6. Direct For Mathematical & Physical Scien [1108802] Funding Source: National Science Foundation

向作者/读者索取更多资源

Methods that exploit galaxy clustering to constrain the galaxy-halo relationship, such as the halo occupation distribution (HOD) and conditional luminosity function (CLF), assume halo mass alone suffices to determine a halo's galaxy content. Yet, halo clustering strength depends upon properties other than mass, such as formation time, an effect known as assembly bias. If galaxy characteristics are correlated with these auxiliary halo properties, the basic assumption of standard HOD/CLF methods is violated. We estimate the potential for assembly bias to induce systematic errors in inferred halo occupation statistics. We construct realistic mock galaxy catalogues that exhibit assembly bias as well as companion mock catalogues with identical HODs, but with assembly bias removed. We fit HODs to the galaxy clustering in each catalogue. In the absence of assembly bias, the inferred HODs describe the true HODs well, validating the methodology. However, in all cases with assembly bias, the inferred HODs exhibit significant systematic errors. We conclude that the galaxy-halo relationship inferred from galaxy clustering is subject to significant systematic errors induced by assembly bias. Efforts to model and/or constrain assembly bias should be priorities as assembly bias is a threatening source of systematic error in galaxy evolution and precision cosmology studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据