English

The DESI $N$-body Simulation Project -- II. Suppressing sample variance with fast simulations

Cosmology and Nongalactic Astrophysics 2022-06-22 v2

Abstract

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches 20\Gpchcube\sim20\Gpchcube. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. \textsc{AbacusSummit} is a suite of high-resolution dark-matter-only simulations designed for this purpose, with 200\Gpchcube200\Gpchcube (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-NN-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. k<0.3\hMpck < 0.3\hMpc for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in \textsc{AbacusSummit} suite to extend the effective volume 20\sim 20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

Keywords

Cite

@article{arxiv.2202.06074,
  title  = {The DESI $N$-body Simulation Project -- II. Suppressing sample variance with fast simulations},
  author = {Zhejie Ding and Chia-Hsun Chuang and Yu Yu and Lehman H. Garrison and Adrian E. Bayer and Yu Feng and Chirag Modi and Daniel J. Eisenstein and Martin White and Andrei Variu and Cheng Zhao and Hanyu Zhang and Jennifer Meneses Rizo and David Brooks and Kyle Dawson and Peter Doel and Enrique Gaztanaga and Robert Kehoe and Alex Krolewski and Martin Landriau and Nathalie Palanque-Delabrouille and Claire Poppett},
  journal= {arXiv preprint arXiv:2202.06074},
  year   = {2022}
}

Comments

Matched version accepted by MNRAS, should be clearer

R2 v1 2026-06-24T09:33:20.245Z