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arXiv · 2007.08472

Density reconstruction from biased tracers and its application to primordial non-Gaussianity

Omar Darwish·Simon Foreman·Muntazir M. Abidi·Tobias Baldauf·Blake D. Sherwin·P. Daniel Meerburg
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Abstract

Large-scale Fourier modes of the cosmic density field are of great value for learning about cosmology because of their well-understood relationship to fluctuations in the early universe. However, cosmic variance generally limits the statistical precision that can be achieved when constraining model parameters using these modes as measured in galaxy surveys, and moreover, these modes are sometimes inaccessible due to observational systematics or foregrounds. For some applications, both limitations can be circumvented by reconstructing large-scale modes using the correlations they induce between smaller-scale modes of an observed tracer (such as galaxy positions). In this paper, we further develop a formalism for this reconstruction, using a quadratic estimator similar to the one used for lensing of the cosmic microwave background. We incorporate nonlinearities from gravity, nonlinear biasing, and local-type primordial non-Gaussianity, and verify that the estimator gives the expected results when applied to N-body simulations. We then carry out forecasts for several upcoming surveys, demonstrating that, when reconstructed modes are included alongside directly-observed tracer density modes, constraints on local primordial non-Gaussianity are generically tightened by tens of percents compared to standard single-tracer analyses. In certain cases, these improvements arise from cosmic variance cancellation, with reconstructed modes taking the place of modes of a separate tracer, thus enabling an effective "multitracer" approach with single-tracer observations.

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Omar Darwish, Simon Foreman, Muntazir M. Abidi, Tobias Baldauf, Blake D. Sherwin, P. Daniel Meerburg. 2020-07-16. Density reconstruction from biased tracers and its application to primordial non-Gaussianity. https://doi.org/10.1103/physrevd.104.123520

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