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Cited by 203

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Renormalized halo bias

Open access
Assassi, Valentin, Baumann, Daniel, Green, Daniel, Zaldarriaga, Matias·2014·Journal of Cosmology and Astroparticle Physics

Abstract

This paper provides a systematic study of renormalization in models of halo biasing. Building on work of McDonald, we show that Eulerian biasing is only consistent with renormalization if non-local terms and higher-derivative contributions are included in the biasing model. We explicitly determine the complete list of required bias parameters for Gaussian initial conditions, up to quartic order in the dark matter density contrast and at leading order in derivatives. At quadratic order, this means including the gravitational tidal tensor, while at cubic order the velocity potential appears as an independent degree of freedom. Our study naturally leads to an effective theory of biasing in which the halo density is written as a double expansion in fluctuations and spatial derivatives. We show that the bias expansion can be organized in terms of Galileon operators which aren't renormalized at leading order in derivatives. Finally, we discuss how the renormalized bias parameters impact the statistics of halos.

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Crossref · 10.1088/1475-7516/2014/08/056

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Metadata

Authors
Assassi, V., Baumann, D., Green, D., & Zaldarriaga, M.
Venue
Journal of Cosmology and Astroparticle Physics
Published
2014-08-27
Volume
2014
Issue
08
Pages
056-056
ISSN
  • 1475-7508 (ISSN-L)
  • 1475-7516
OpenAlex
W1966924254
Cited by
203
References
3
FWCI
5.82
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Renormalized halo bias. https://app.cochat.ai/papers/10.1088/1475-7516/2014/08/056
The paper
Assassi, V., Baumann, D., Green, D., & Zaldarriaga, M. (2014). Renormalized halo bias. Journal of Cosmology and Astroparticle Physics. https://doi.org/10.1088/1475-7516/2014/08/056

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.