Covariance matrices for variance-suppressed simulations

• Published in: Monthly notices of the Royal Astronomical Society 2023-01, Vol.518 (3), p.3737-3745
• Main Authors: Zhang, Tony, Chuang, Chia-Hsun, Wechsler, Risa H, Alam, Shadab, DeRose, Joseph, Feng, Yu, Kitaura, Francisco-Shu, Pellejero-Ibanez, Marcos, Rodríguez-Torres, Sergio, To, Chun-Hao, Yepes, Gustavo, Zhao, Cheng
• Format: Article
• Language: English
• Subjects: ASTRONOMY AND ASTROPHYSICS; cosmology: theory; galaxies: haloes; large-scale structure of Universe; methods: numerical; software: simulations
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stac3261

Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that ezmock (Effective Zel’dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the ezmock bias model.