CWRU PAT Coffee Agenda

Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30

+3 Iterative initial condition reconstruction.

mro28 +1 jtd55 +1 jbm120 +1

+1 A tale of two modes: Neutrino free-streaming in the early universe.

sxk1031 +1

+1 GPS as a dark matter detector: Orders-of-magnitude improvement on couplings of clumpy dark matter to atomic clocks.

jtd55 +1

+1 Testing general relativity using gravitational wave signals from the inspiral, merger and ringdown of binary black holes.

cxt282 +1

Showing votes from 2017-04-21 12:30 to 2017-04-25 11:30 | Next meeting is Tuesday May 19th, 10:30 am.

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astro-ph.CO

  • Iterative initial condition reconstruction.- [PDF] - [Article]

    Marcel Schmittfull, Tobias Baldauf, Matias Zaldarriaga
     

    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 iteratively moved back along estimated potential gradients 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 non-linear 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 $k\le 0.35\; h\mathrm{Mpc}^{-1}$. The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than two orders of magnitude at $k\le 0.2\; h\mathrm{Mpc}^{-1}$, and it extends the range of scales where the full broad-band shape of the power spectrum matches linear theory by a factor 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. We find that the method improves the BAO signal-to-noise by a factor 2.7 at redshift $z=0$ and by a factor 2.5 at $z=0.6$ in our idealistic simulations. This improves standard BAO reconstruction by $70\%$ at $z=0$ and $30\%$ at $z=0.6$, and matches the optimal BAO signal and signal-to-noise of the linear density in the same volume.

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