Issue |
A&A
Volume 669, January 2023
|
|
---|---|---|
Article Number | A6 | |
Number of page(s) | 5 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202244986 | |
Published online | 20 December 2022 |
Suppressing variance in 21 cm signal simulations during reionization
1
Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
e-mail: sambit.giri@ics.uzh.ch
2
Donostia International Physics Center (DIPC), Paseo Manuel de Lardizabal, 4, 20018 Donostia-San Sebastiàn, Spain
3
IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
Received:
16
September
2022
Accepted:
25
October
2022
Current best limits on the 21 cm signal during reionization are provided at large scales (≳100 Mpc). To model these scales, enormous simulation volumes are required, which are computationally expensive. We find that the primary source of uncertainty at these large scales is sample variance, which determines the minimum size of simulations required to analyse current and upcoming observations. In large-scale structure simulations, the method of ‘fixing’ the initial conditions (ICs) to exactly follow the initial power spectrum and ‘pairing’ two simulations with exactly out-of-phase ICs has been shown to significantly reduce sample variance. Here we apply this ‘fixing and pairing’ (F&P) approach to reionization simulations whose clustering signal originates from both density fluctuations and reionization bubbles. Using a semi-numerical code, we show that with the traditional method, simulation boxes of L ≃ 500 (300) Mpc are required to model the large-scale clustering signal at k = 0.1 Mpc−1 with a precision of 5 (10)%. Using F&P, the simulation boxes can be reduced by a factor of 2 to obtain the same precision level. We conclude that the computing costs can be reduced by at least a factor of 4 when using the F&P approach.
Key words: dark ages / reionization / first stars / cosmology: theory / Galaxy: formation / intergalactic medium
© The Authors 2022
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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