%0 Generic %1 ding2022nbody %A Ding, Zhejie %A Chuang, Chia-Hsun %A Yu, Yu %A Garrison, Lehman H. %A Bayer, Adrian E. %A Feng, Yu %A Modi, Chirag %A Eisenstein, Daniel J. %A White, Martin %A Variu, Andrei %A Zhao, Cheng %A Zhang, Hanyu %A Rizo, Jennifer Meneses %A Brooks, David %A Dawson, Kyle %A Doel, Peter %A Gaztanaga, Enrique %A Kehoe, Robert %A Krolewski, Alex %A Landriau, Martin %A Palanque-Delabrouille, Nathalie %A Poppett, Claire %D 2022 %K library %T The DESI $N$-body Simulation Project II: Suppressing Sample Variance with Fast Simulations %U http://arxiv.org/abs/2202.06074 %X Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise 3D map of our Universe. The survey effective volume reaches $\sim20$ Gpc$^3h^-3$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200$ Gpc$^3h^-3$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide 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-$N$-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variances than the expected DESI statistical variance at the scales we are interested in, e.g., $k < 0.3~h$Mpc$^-1$. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume $\sim 20$ 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.

@misc{ding2022nbody, abstract = {Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise 3D map of our Universe. The survey effective volume reaches $\sim20$ Gpc$^3h^{-3}$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200$ Gpc$^3h^{-3}$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide 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-$N$-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variances than the expected DESI statistical variance at the scales we are interested in, e.g., $k < 0.3~h$Mpc$^{-1}$. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume $\sim 20$ 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.}, added-at = {2022-02-15T04:57:04.000+0100}, author = {Ding, Zhejie and Chuang, Chia-Hsun and Yu, Yu and Garrison, Lehman H. and Bayer, Adrian E. and Feng, Yu and Modi, Chirag and Eisenstein, Daniel J. and White, Martin and Variu, Andrei and Zhao, Cheng and Zhang, Hanyu and Rizo, Jennifer Meneses and Brooks, David and Dawson, Kyle and Doel, Peter and Gaztanaga, Enrique and Kehoe, Robert and Krolewski, Alex and Landriau, Martin and Palanque-Delabrouille, Nathalie and Poppett, Claire}, biburl = {https://www.bibsonomy.org/bibtex/28ae2aa31d8e32c1fcaf81470b960521d/gpkulkarni}, description = {The DESI $N$-body Simulation Project II: Suppressing Sample Variance with Fast Simulations}, interhash = {15824bdd695b9a33e69a715bb45be0ee}, intrahash = {8ae2aa31d8e32c1fcaf81470b960521d}, keywords = {library}, note = {cite arxiv:2202.06074Comment: 20 pages, 25 figures, 1 table}, timestamp = {2022-02-15T04:57:04.000+0100}, title = {The DESI $N$-body Simulation Project II: Suppressing Sample Variance with Fast Simulations}, url = {http://arxiv.org/abs/2202.06074}, year = 2022 }