Generating approximate halo catalogues for blind challenges in precision cosmology
Abstract
We present a method for generating suites of dark matter halo catalogues with only a few N-body simulations, focusing on making small changes to the underlying cosmology of a simulation with high precision. In the context of blind challenges, this allows us to re-use a simulation by giving it a new cosmology after the original cosmology is revealed. Starting with full N-body realizations of an original cosmology and a target cosmology, we fit a transfer function that displaces haloes in the original so that the galaxy/HOD power spectrum matches that of the target cosmology. This measured transfer function can then be applied to a new realization of the original cosmology to create a new realization of the target cosmology. For a 1 percent change in σ8, we achieve 0.1 percent accuracy to |$$k = 1\, h\, \mathrm{Mpc}^{-1}$$| in the real-space power spectrum; this degrades to 0.3 percent when the transfer function is applied to a new realization. Here, we achieve similar accuracy in the redshift-space monopole and quadrupole. In all cases, the result is better than the sample variance of our |$$1.1\, h^{-1}\, \mathrm{Gpc}$$| simulation boxes.
- Publication Date:
- Research Org.:
- Harvard Univ., Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Science Foundation (NSF)
- OSTI Identifier:
- 1498594
- Alternate Identifier(s):
- OSTI ID: 1612028
- Grant/Contract Number:
- SC0013718; AST-1313285
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 485 Journal Issue: 2; Journal ID: ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; astronomy & astrophysics; methods: numerical; large-scale structure of Universe
Citation Formats
Garrison, Lehman H., and Eisenstein, Daniel J. Generating approximate halo catalogues for blind challenges in precision cosmology. United Kingdom: N. p., 2019.
Web. doi:10.1093/mnras/stz600.
Garrison, Lehman H., & Eisenstein, Daniel J. Generating approximate halo catalogues for blind challenges in precision cosmology. United Kingdom. https://doi.org/10.1093/mnras/stz600
Garrison, Lehman H., and Eisenstein, Daniel J. Sat .
"Generating approximate halo catalogues for blind challenges in precision cosmology". United Kingdom. https://doi.org/10.1093/mnras/stz600.
@article{osti_1498594,
title = {Generating approximate halo catalogues for blind challenges in precision cosmology},
author = {Garrison, Lehman H. and Eisenstein, Daniel J.},
abstractNote = {We present a method for generating suites of dark matter halo catalogues with only a few N-body simulations, focusing on making small changes to the underlying cosmology of a simulation with high precision. In the context of blind challenges, this allows us to re-use a simulation by giving it a new cosmology after the original cosmology is revealed. Starting with full N-body realizations of an original cosmology and a target cosmology, we fit a transfer function that displaces haloes in the original so that the galaxy/HOD power spectrum matches that of the target cosmology. This measured transfer function can then be applied to a new realization of the original cosmology to create a new realization of the target cosmology. For a 1 percent change in σ8, we achieve 0.1 percent accuracy to |$k = 1\, h\, \mathrm{Mpc}^{-1}$| in the real-space power spectrum; this degrades to 0.3 percent when the transfer function is applied to a new realization. Here, we achieve similar accuracy in the redshift-space monopole and quadrupole. In all cases, the result is better than the sample variance of our |$1.1\, h^{-1}\, \mathrm{Gpc}$| simulation boxes.},
doi = {10.1093/mnras/stz600},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 485,
place = {United Kingdom},
year = {2019},
month = {3}
}
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