The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approximation schemes

By D. Blas, Julien Lesgourgues, Thomas Tram. 2011

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Boltzmann codes are used extensively by several groups for constraining cosmological parameters with Cosmic Microwave Background and Large Scale Structure data. This activity is computationally expensive, since a typical project requires from 104 to 105 Boltzmann code executions. The newly released code CLASS (Cosmic Linear Anisotropy Solving System) incorporates improved approximation schemes leading to a simultaneous gain in speed and precision. We describe here the three approximations used by CLASS for basic ΛCDM models, namely: a baryon-photon tight-coupling approximation which can be set to first order, second order or to a compromise between the two; an ultra-relativistic fluid approximation which had not been implemented in public distributions before; and finally a radiation streaming approximation taking reionisation into account.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/2fd1a04a2466f851f162cdf8c6c4c3ecc9d88e3c

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Citations in the corpus

Phases of new physics in the CMB (2015, methodology): “een solid and dashed lines are the dierences in the unlensed and lensed data, respectively. We see that the change is largest in the unlensed EE spectrum. Our modication of the Boltzmann code CLASS [50] includes an additional relativistic uid, whose energy density is measured by the parameter N uid ”
New observational constraints on f(R) gravity from cosmic chronometers (2016, methodology): “publicly available code CLASS [90] in the interface with the public Monte Carlo code Monte Python [91].”
H0LiCOW - V. New COSMOGRAIL time delays of HE 0435-1223: H0 to 3.8 per cent precision from strong lensing in a flat ΛCDM model (2016, methodology): “Temperature power spectra were computed using the Cosmic Linear Anisotropy Solving System Boltzman code (CLASS; Blas et al. 2011; Lesgourgues & Tram 2014) and MCMC chains were generated with the MontePython sampler (Audren et al. 2013).”
Nonlocal gravity. Conceptual aspects and cosmological predictions (2017, methodology): “ely how the model ts the data in comparison with CDM. For the RR model this has been done in [30{33], and here we will expand on these results. We have implemented the cosmological equations in CLASS [116], and constrained the nonlocal model with observations using the Markov Chain Monte Carlo (MCMC”