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2017

Iterative initial condition reconstruction

Schmittfull, Marcel, Baldauf, Tobias, Zaldarriaga, Matias

Understand

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.