Constraints on the Fluctuation Amplitude and Density Parameter from X-Ray Cluster Number Counts

Constraints on the Fluctuation Amplitude and Density Parameter from X-Ray Cluster Number Counts
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X射线簇数计数波动幅度和密度参数的约束

DOI:
10.1086/304915
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发表时间:
1997
期刊:
The Astrophysical Journal
影响因子:
--
通讯作者:
Yasushi Suto
Yasushi Suto
中科院分区:
--
文献类型:
--
作者:
T. Kitayama;Yasushi Suto

文献摘要

被引文献

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我们发现在冷暗物质(CDM)宇宙中,观测到的x射线星团的log N-log S关系可以在一定范围的波动幅度σ8和宇宙密度参数Ω0的值下很好地再现。在n = 1和h = 0.7的CDM模型中,σ8的1 σ置信限表示为(0.54±0.02)Ω (λ0 = 1 - Ω0)和(0.54±0.02)Ω (λ0 = 0),其中n为原始光谱指数,h和λ0为无量纲哈勃常数和宇宙学常数。上面引用的误差仅表示观测到的log N-log S的统计误差;在σ8的最佳拟合值范围内,x射线通量理论模型的系统不确定度约为15%。在n = 1的情况下,我们发现(Ω0, λ0, h, σ8)≃(0.3,0.7,0.7,1)和(0.45,0,0.7,0.8)的CDM模型同时可以解释聚类log n -log S、x射线温度函数和COBE 4年数据的归一化。推导值假设观测值没有残余系统误差,并详细讨论了可能改变log N-log S关系Ω0和σ8极限的其他理论不确定性。我们展示了这种新方法的力量,随着观测精度的提高,它将成为一种强有力的工具。
We find that the observed log N-log S relation of X-ray clusters can be reproduced remarkably well with a certain range of values for the fluctuation amplitude σ8 and the cosmological density parameter Ω0 in cold dark matter (CDM) universes. The 1 σ confidence limits on σ8 in the CDM models with n = 1 and h = 0.7 are expressed as (0.54±0.02)Ω (λ0 = 1 - Ω0) and (0.54 ± 0.02)Ω (λ0 = 0), where n is the primordial spectral index, and h and λ0 are the dimensionless Hubble and cosmological constants. The errors quoted above indicate statistical errors from the observed log N-log S only; the systematic uncertainty from our theoretical modeling of X-ray flux in the best-fit value of σ8 is about 15%. In the case where n = 1, we find that the CDM models with (Ω0, λ0, h, σ8) ≃ (0.3, 0.7, 0.7, 1) and (0.45, 0, 0.7, 0.8) simultaneously account for the cluster log N-log S, X-ray temperature functions, and the normalization from the COBE 4 year data. The derived values assume that the observations are without residual systematic errors, and we discuss in detail other theoretical uncertainties that may change the limits on Ω0 and σ8 from the log N-log S relation. We show the power of this new approach, which will become a strong tool as observations attain more precision.