Wang, Yuchan and Li, Baojiu and Cautun, Marius (2020) 'Iterative removal of redshift space distortions from galaxy clustering.', Monthly notices of the Royal Astronomical Society,, 497 (3). pp. 3451-3471.
Observations of galaxy clustering are made in redshift space, which results in distortions to the underlying isotropic distribution of galaxies. These redshift-space distortions (RSD) not only degrade important features of the matter density field, such as the baryonic acoustic oscillation (BAO) peaks, but also pose challenges for the theoretical modelling of observational probes. Here we introduce an iterative nonlinear reconstruction algorithm to remove RSD effects from galaxy clustering measurements, and assess its performance by using mock galaxy catalogues. The new method is found to be able to recover the real-space galaxy correlation function with an accuracy of ∼1%, and restore the quadrupole accurately to 0, on scales s≳ 20 h−1Mpc. It also leads to an improvement in the reconstruction of the initial density field, which could help to accurately locate the BAO peaks. An ‘internal calibration’ scheme is proposed to determine the values of cosmological parameters as a part of the reconstruction process, and possibilities to break parameter degeneracies are discussed. RSD reconstruction can offer a potential way to simultaneously extract the cosmological parameters, initial density field, real-space galaxy positions and large-scale peculiar velocity field (of the real Universe), making it an alternative to standard perturbative approaches in galaxy clustering analysis, bypassing the need for RSD modelling.
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|Publisher Web site:||https://doi.org/10.1093/mnras/staa2136|
|Publisher statement:||This article has been accepted for publication in Monthly notices of the Royal Astronomical Society. ©: 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.|
|Date accepted:||10 July 2020|
|Date deposited:||21 August 2020|
|Date of first online publication:||24 July 2020|
|Date first made open access:||21 August 2020|
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