The evolution of galaxy clustering
The evolution of galaxy clustering
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DOI：
10.1093/mnras/284.4.885
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发表时间：
1996-08
影响因子：
4.8
通讯作者：
J. Peacock
中科院分区：
文献类型：
--
作者：
J. Peacock
本文研究了非线性引力不稳定性是否能解释星系团的大小尺度演化，以及星系团随时间的演化。局部聚类谱是准确建立的，偏差不是一个很大的不确定性来源：光学和lRAS星系的实空间功率谱只显示出一个弱的尺度依赖的相对偏差B = 1.15的大尺度上，增加到B = 1.5的最小尺度。与在红移空间中的结果的比较有利于相对小的失真参数{3 opt 51（} 0.6/bopt = 0.4。没有类似CDM的光谱与观察到的非线性光谱的形状一致。无偏的低密度模型大大高估了小规模的相关性;高密度模型将需要一个偏差，不随规模单调变化。真正的线性功率谱包含在k = 0. lhMpc-1处的原始特征，并且在较小尺度上必须相当突然地断裂到n：：s-2.3的有效斜率。这个经验涨落谱也符合CFRS关于成团演化的数据，前提是宇宙是开放的，（）= 0.3。只有这种情况才能自然地解释小尺度谱如何以观测到的速率演化，同时保持相同的幂律指数。一个无偏的开放模型也正确地匹配大规模的COBE数据，并提供了一个有吸引力的简单的图片星系集群的现象。
This paper investigates whether non-linear gravitational instability can account for the clustering of galaxies on large and small scales, and for the evolution of clustering with epoch. The local clustering spectrum is accurately established, and bias is not a great source of uncertainty: the real-space power spectra of optical and lRAS galaxies show only a weak scale dependent relative bias of b = 1.15 on large scales, increasing to b = 1.5 on the smallest scales. Comparison with results in redshift space favours a relatively small distortion parameter {3opt 51 (}o.6/bopt = 0.4. No CDM-like spectrum is consistent with the shape of the observed non-linear spectrum. Unbiased low-density models greatly overpredict the small-scale correlations; high-density models would require a bias which does not vary monotonically with scale. The true linear power spectrum contains a primordial feature at k = O.lhMpc-l, and must break quite abruptly to an effective slope of n ::s -2.3 on smaller scales. This empirical fluctuation spectrum also fits the CFRS data on the evolution of clustering, provided that the Universe is open with (} = 0.3. Only this case explains naturally how the small-scale spectrum can evolve at the observed rate while retaining the same power-law index. An unbiased open model also matches correctly the large-scale COBE data, and offers an attractively simple picture for the phenomenology of galaxy clustering.