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Subhalo abundance matching in f(R) gravity

He, Jian-hua; Li, Baojiu; Baugh, Carlton M.

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Jian-hua He


Using the liminality N-body simulations of Shi et al., we present the first predictions for galaxy clustering in f(R) gravity using subhalo abundance matching. We find that, for a given galaxy density, even for an f(R) model with fR0=−10−6, for which the cold dark matter clustering is very similar to the cold dark matter model with a cosmological constant (ΛCDM), the predicted clustering of galaxies in the f(R) model is very different from ΛCDM. The deviation can be as large as 40% for samples with mean densities close to that of L∗ galaxies. This large deviation is testable given the accuracy that future large-scale galaxy surveys aim to achieve. Our result demonstrates that galaxy surveys can provide a stringent test of general relativity on cosmological scales, which is comparable to the tests from local astrophysical observations.


He, J., Li, B., & Baugh, C. M. (2016). Subhalo abundance matching in f(R) gravity. Physical Review Letters, 117(22), Article 221101.

Journal Article Type Article
Acceptance Date Nov 1, 2016
Online Publication Date Nov 21, 2016
Publication Date Nov 21, 2016
Deposit Date Aug 21, 2016
Publicly Available Date Dec 7, 2016
Journal Physical Review Letters
Print ISSN 0031-9007
Electronic ISSN 1079-7114
Publisher American Physical Society
Peer Reviewed Peer Reviewed
Volume 117
Issue 22
Article Number 221101
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Published Journal Article (362 Kb)

Copyright Statement
Reprinted with permission from the American Physical Society: Physical Review Letters 117, 221101 © (2016) by the American Physical Society. Readers may view, browse, and/or download material for temporary copying purposes only, provided these uses are for noncommercial personal purposes. Except as provided by law, this material may not be further reproduced, distributed, transmitted, modified, adapted, performed, displayed, published, or sold in whole or part, without prior written permission from the American Physical Society.

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