McCullagh, N. and Neyrinck, M. and Norberg, P. and Cole, S. (2016) 'Recovering dark-matter clustering from galaxies with Gaussianization.', Monthly notices of the Royal Astronomical Society., 457 (4). pp. 3652-3665.
The Gaussianization transform has been proposed as a method to remove the issues of scale-dependent galaxy bias and non-linearity from galaxy clustering statistics, but these benefits have yet to be thoroughly tested for realistic galaxy samples. In this paper, we test the effectiveness of the Gaussianization transform for different galaxy types by applying it to realistic simulated blue and red galaxy samples. We show that in real space, the shapes of the Gaussianized power spectra of both red and blue galaxies agree with that of the underlying dark matter, with the initial power spectrum, and with each other to smaller scales than do the statistics of the usual (untransformed) density field. However, we find that the agreement in the Gaussianized statistics breaks down in redshift space. We attribute this to the fact that red and blue galaxies exhibit very different fingers of god in redshift space. After applying a finger-of-god compression, the agreement on small scales between the Gaussianized power spectra is restored. We also compare the Gaussianization transform to the clipped galaxy density field and find that while both methods are effective in real space, they have more complicated behaviour in redshift space. Overall, we find that Gaussianization can be useful in recovering the shape of the underlying dark-matter power spectrum to k ∼ 0.5 h Mpc−1 and of the initial power spectrum to k ∼ 0.4 h Mpc−1 in certain cases at z = 0.
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|Publisher Web site:||http://dx.doi.org/10.1093/mnras/stw223|
|Publisher statement:||This article has been published in Monthly Notices of the Royal Astronomical Society ©: 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.|
|Date accepted:||23 January 2016|
|Date deposited:||07 April 2016|
|Date of first online publication:||27 January 2016|
|Date first made open access:||07 April 2016|
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