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Cited by 381

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The Cosmic Linear Anisotropy Solving System (CLASS) IV: efficient implementation of non-cold relics

Open access
Lesgourgues, Julien, Tram, Thomas·2011·Journal of Cosmology and Astroparticle Physics

Abstract

We present a new flexible, fast and accurate way to implement massive neutrinos, warm dark matter and any other non-cold dark matter relics in Boltzmann codes. For whatever analytical or numerical form of the phase-space distribution function, the optimal sampling in momentum space compatible with a given level of accuracy is automatically found by comparing quadrature methods. The perturbation integration is made even faster by switching to an approximate viscous fluid description inside the Hubble radius, which differs from previous approximations discussed in the literature. When adding one massive neutrino to the minimal cosmological model, CLASS becomes just 1.5 times slower, instead of about 5 times in other codes (for fixed accuracy requirements). We illustrate the flexibility of our approach by considering a few examples of standard or non-standard neutrinos, as well as warm dark matter models.

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Crossref · 10.1088/1475-7516/2011/09/032

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Metadata

Authors
Lesgourgues, J., & Tram, T.
Venue
Journal of Cosmology and Astroparticle Physics
Published
2011-09-27
Volume
2011
Issue
09
Pages
032-032
ISSN
  • 1475-7508 (ISSN-L)
  • 1475-7516
OpenAlex
W2164089543
Cited by
381
References
1
FWCI
4.15
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The Cosmic Linear Anisotropy Solving System (CLASS) IV: efficient implementation of non-cold relics. https://app.cochat.ai/papers/10.1088/1475-7516/2011/09/032
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Lesgourgues, J., & Tram, T. (2011). The Cosmic Linear Anisotropy Solving System (CLASS) IV: efficient implementation of non-cold relics. Journal of Cosmology and Astroparticle Physics. https://doi.org/10.1088/1475-7516/2011/09/032

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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.