Journal
PHYSICAL REVIEW D
Volume 96, Issue 2, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.96.023505
Keywords
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Funding
- Science and Technology Facilities Council [ST/K00333X/1, ST/P000673/1, ST/L000636/1, ST/J005673/1, ST/M007065/1] Funding Source: researchfish
- Direct For Mathematical & Physical Scien
- Division Of Astronomical Sciences [1409709] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1521097] Funding Source: National Science Foundation
- STFC [ST/L000636/1, ST/P000673/1, ST/J005673/1, ST/K00333X/1, ST/M007065/1] Funding Source: UKRI
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Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift z = 0, we find that after eight iterations the reconstructed density is more than 95% correlated with the initial density at k <= 0.35 hMpc(-1). The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at k <= 0.2 hMpc(-1), and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2-3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at z = 0 and by a factor of 2.5 at z = 0.6, improving standard BAO reconstruction by 70% at z = 0 and 30% at z = 0.6, and matching the optimal BAO signal and signalto- noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.
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