Detecting baryon acoustic oscillations in dark matter from kinematic weak lensing surveys
Abstract We investigate the feasibility of extracting baryon acoustic oscillations (BAO) from cosmic shear tomography. We particularly focus on the BAO scale precision that can be achieved by future spectroscopy-based, kinematic weak lensing (KWL) surveys in comparison to the traditional photometry-...
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Published in: | Monthly notices of the Royal Astronomical Society Vol. 487; no. 1; pp. 253 - 267 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United Kingdom
Oxford University Press
21-07-2019
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Subjects: | |
Online Access: | Get full text |
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Summary: | Abstract
We investigate the feasibility of extracting baryon acoustic oscillations (BAO) from cosmic shear tomography. We particularly focus on the BAO scale precision that can be achieved by future spectroscopy-based, kinematic weak lensing (KWL) surveys in comparison to the traditional photometry-based weak lensing surveys. We simulate cosmic shear tomography data of such surveys with a few simple assumptions to focus on the BAO information, extract the spatial power spectrum, and constrain the recovered BAO feature. Due to the small shape noise and the shape of the lensing kernel, we find that a Dark Energy Task Force Stage IV version of such KWL survey can detect the BAO feature in dark matter by 3σ and measure the BAO scale at the precision level of 4 per cent, while it will be difficult to detect the feature in photometry-based weak lensing surveys. With a more optimistic assumption, a KWL-Stage IV could achieve a ${\sim } 2{{\ \rm per\ cent}}$ BAO scale measurement with 4.9σ confidence. A built-in spectroscopic galaxy survey within such KWL survey will allow cross-correlation between galaxies and cosmic shear, which will tighten the constraint beyond the lower limit we present in this paper and therefore possibly allow a detection of the BAO scale bias between galaxies and dark matter. |
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Bibliography: | SC0014329; AC02-05CH11231 USDOE Office of Science (SC) |
ISSN: | 0035-8711 1365-2966 |
DOI: | 10.1093/mnras/stz1257 |