4.6 Article

Mass bias evolution in tSZ cluster cosmology

期刊

ASTRONOMY & ASTROPHYSICS
卷 626, 期 -, 页码 -

出版社

EDP SCIENCES S A
DOI: 10.1051/0004-6361/201935041

关键词

large-scale structure of Universe; galaxies: clusters: general; cosmological parameters

资金

  1. Centre National d'Etudes Spatiales (CNES)
  2. NSERC's Discovery Grant programme
  3. ESA Member States
  4. NASA
  5. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [ERC-2015-AdG 695561]

向作者/读者索取更多资源

Galaxy clusters observed through the thermal Sunyaev-Zeldovich (tSZ) effect are a recent cosmological probe. The precision on the cosmological constraints is affected mainly by the current knowledge of cluster physics, which enters the analysis through the scaling relations. Here we aim to study one of the most important sources of systematic uncertainties, the mass bias, b. We have analysed the effects of a mass-redshift dependence, adopting a power-law parametrisation. We applied this parametrisation to the combination of tSZ number counts and power spectrum, finding a hint of redshift dependence that leads to a decreasing value of the mass bias for higher redshift. We tested the robustness of our results for different mass bias calibrations and a discrete redshift dependence. We find our results to be dependent on the clusters sample that we are considering, in particular obtaining an inverse (decreasing) redshift dependence when neglecting z < 0.2 clusters. We analysed the effects of this parametrisation on the combination of cosmic microwave background (CMB) primary anisotropies and tSZ galaxy clusters. We find a preferred constant value of mass bias, having (1 - b) = 0.62 +/- 0.05. The corresponding value of b is too high with respect to weak lensing and numerical simulations estimations. Therefore we conclude that this mass-redshift parametrisation does not help in solving the remaining discrepancy between CMB and tSZ clusters observations.

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

4.6
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