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Open AccessJournal ArticleDOI

OUP accepted manuscript

- 23 Mar 2022 - 
- Vol. 512, Iss: 4, pp 5311-5328
TLDR
In this article , a simulated likelihood analysis of the galaxy clustering and weak lensing data from the Roman Space Telescope High Latitude Survey combined with CMB lensing from the Simons Observatory is undertaken, marginalizing over important astrophysical effects and calibration uncertainties.
Abstract
We explore synergies between the Nancy Grace Roman Space Telescope and CMB lensing data to constrain dark energy and modified gravity scenarios. A simulated likelihood analysis of the galaxy clustering and weak lensing data from the Roman Space Telescope High Latitude Survey combined with CMB lensing data from the Simons Observatory is undertaken, marginalizing over important astrophysical effects and calibration uncertainties. Included in the modeling are the effects of baryons on small-scale clustering, scale-dependent growth suppression by neutrinos, as well as uncertainties in the galaxy clustering biases, in the intrinsic alignment contributions to the lensing signal, in the redshift distributions, and in the galaxy shape calibration. The addition of CMB lensing roughly doubles the dark energy figure-of-merit from Roman photometric survey data alone, varying from a factor of 1.7 to 2.4 improvement depending on the particular Roman survey configuration. Alternatively, the inclusion of CMB lensing information can compensate for uncertainties in the Roman galaxy shape calibration if it falls below the design goals. Furthermore, we report the first forecast of Roman constraints on a model-independent structure growth, parameterized by $\sigma_8 (z)$, and on the Hu-Sawicki f(R) gravity as well as an improved forecast of the phenomenological $(\Sigma_0,\mu_0)$ model. We find that CMB lensing plays a crucial role in constraining $\sigma_8(z)$ at z>2, with percent-level constraints forecasted out to z=4. CMB lensing information does not improve constraints on the f(R) models substantially. It does, however, increase the $(\Sigma_0,\mu_0)$ figure-of-merit by a factor of about 1.5.

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