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3\%-accurate predictions for the clustering of dark matter, haloes and subhaloes, over a wide range of cosmologies and scales

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arxiv 2001.03176 v2 pith:2MCECQVA submitted 2020-01-09 astro-ph.CO

classification astro-ph.CO
keywords darkhaloesmatterprecisionsubhaloesclusteringfunctionpredictions
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abstract

Predicting the spatial distribution of objects as a function of cosmology is an essential ingredient for the exploitation of future galaxy surveys. In this paper we show that a specially-designed suite of gravity-only simulations together with cosmology-rescaling algorithms can provide the clustering of dark matter, haloes, and subhaloes with high precision. Specifically, with only 3 $N$-body simulations we obtain the power spectrum of dark matter at $z=0$ and $z=1$ to better than 3\% precision for essentially all currently viable values of 8 cosmological parameters, including massive neutrinos and dynamical dark energy, and over the whole range of scales explored, 0.03 < $k/h^{-1}Mpc$ < 5. This precision holds at the same level for mass-selected haloes and for subhaloes selected according to their peak maximum circular velocity. As an initial application of these predictions, we successfully constrain $\Omega_{\rm m}$, $\sigma_8$, and the scatter in subhalo-abundance-matching employing the projected correlation function of mock SDSS galaxies.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    A neural ODE trained only on LambdaCDM spectra predicts nonlinear matter power spectra to about 4 percent accuracy for smooth w(z) dark energy models, pending stronger validation.

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