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Simulated reconstruction of the remote dipole field using the kinetic Sunyaev Zeldovich effect

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 نشر من قبل Matthew Johnson
 تاريخ النشر 2018
  مجال البحث فيزياء
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The kinetic Sunyaev Zeldovich (kSZ) effect, cosmic microwave background (CMB) anisotropies induced by scattering from free electrons in bulk motion, is a primary target of future CMB experiments. Measurements of the kSZ effect have the potential to address fundamental questions about the structure and evolution of our Universe on the largest scales and at the earliest times. This potential is unlocked by combining measurements of small-scale CMB anisotropies with large-scale structure surveys, a technique known as kSZ tomography. Previous work established a quadratic estimator for the remote dipole field, the CMB dipole observed at different locations in the Universe. This previous work did not include gravitational lensing, redshift space distortions, or non-linear evolution of structure. In this paper, we investigate how well the remote dipole field can be reconstructed in the presence of such effects by using mock data from a suite of simulations. To properly model both large and small scales, we develop a novel box-in-box simulation pipeline, where small-scale information is obtained from N-body simulations, and large-scale information obtained by evolving fields using linear theory and adding the resulting corrections to the N-body particle data. This pipeline allows us to create properly correlated maps of the primary CMB including lensing, as well as the kSZ effect and density maps on the past light cone of an observer. Analyzing an ensemble of mocks, we find that the dipole field can be reconstructed with high fidelity over a range of angular scales and redshift bins, although there is evidence of excess power from nonlinear structure. We also analyze correlations with the primary CMB, investigating the ability of kSZ tomography to reconstruct the intrinsic CMB dipole. Our results constitute a proof-of-principle that kSZ tomography is a promising technique for future datasets.



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