Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

Abstract

We perform an ensemble of N-body simulations with 2048 3 particles for 101 flat wCDM cosmological models sampled based on a maximin-distance Sliced Latin Hypercube Design. By using the halo catalogs extracted at multiple redshifts in the range of z = [0, 1.48], we develop Dark Emulator, which enables fast and accurate computations of the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models, based on the Principal Component Analysis and the Gaussian Process Regression for the large-dimensional input and output data vector. We assess the performance of the emulator using a validation set of N-body simulations that are not used in training the emulator. We show that, for typical halos hosting CMASS galaxies in the Sloan Digital Sky Survey, the emulator predicts the halo-matter cross correlation, relevant for galaxy-galaxy weak lensing, with an accuracy better than 2% and the halo auto-correlation, relevant for galaxy clustering correlation, with an accuracy better than 4%. We give several demonstrations of the emulator. It can be used to study properties of halo mass density profiles such as the mass-concentration relation and splashback radius for different cosmologies. The emulator outputs can be combined with an analytical prescription of halo-galaxy connection such as the halo occupation distribution at the equation level, instead of using the mock catalogs, to make accurate predictions of galaxy clustering statistics such as the galaxy-galaxy weak lensing and the projected correlation function for any model within the wCDM cosmologies, in a few CPU seconds. Subject headings: large-scale structure of the universe -numerical simulations -machine learning

A rigorous EFT-based forward model for large-scale structure

“…We will defer this study to future work; see e.g. [53][54][55] for in-depth discussions of transients.…”

Euclid preparation: IX. EuclidEmulator2 – power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

“…We sample the points in the parameter space defined in Table 2 using LHS, as we have done already in Euclid Collaboration: Knabenhans et al (2019). LHS is a very straightforward sampling technique that is widely used and accepted in the cosmological emulator community (Heitmann et al 2009(Heitmann et al , 2010(Heitmann et al , 2014Nishimichi et al 2019;DeRose et al 2019;Gration & Wilkinson 2019;Rogers et al 2019) and extensively presented in the statistical sampling literature (McKay et al 1979;Tang 1993;Liefvendahl & Stocki 2006;Crombecq et al 2011;Damblin et al 2013;Sheikholeslami & Razavi 2017;Yang et al 2017;Garg & Stogner 2017;Swiler et al 2006). Endowed with an additional optimisation step (we use a distance-based criterion), its main advantage is that it combines good space-filling properties with a high degree of randomness.…”

“…Several emulators are available already. Examples are FrankenEmu (Heitmann et al 2009(Heitmann et al , 2010(Heitmann et al , 2014, CosmicEmu (Heitmann et al 2016;Lawrence et al 2017), the emulators of the Aemulus project (DeRose et al 2019;McClintock et al 2019;Zhai et al 2019), NGenHalofit (Smith & Angulo 2019), EuclidEmulator1 (Euclid Collaboration: Knabenhans et al 2019), the Dark quest emulator (Nishimichi et al 2019) and BE-HaPPY (Valcin et al 2019).…”

Weak lensing minima and peaks: Cosmological constraints and the impact of baryons

“…In order to evaluate the statistics at arbitrary cosmologies, we construct an emulator from the 101 simulated cosmologies using Gaussian Processes (Heitmann et al 2009;Kwan et al 2013Kwan et al , 2015Heitmann et al 2016;Nishimichi et al 2019;McClintock et al 2019), with the triaxial squared exponential kernel. We can then interpolate the statistics to any cosmology within the sample parameter ranges.…”