Numerical Cosmology - NumCosmo. The NumCosmo is a free software C library whose main purposes are to test cosmological models using observational data and to provide a set of tools to perform cosmological calculations. Particularly, the current version has implemented three different probes: cosmic microwave background (CMB),supernovae type Ia (SNeIa) and large scale structure (LSS) information, such as baryonic acoustic oscillations (BAO) and galaxy cluster abundance. The code supports a joint analysis of these data and the parameter space can include cosmological and phenomenological parameters. It is worth emphasizing that NumCosmo matter power spectrum and CMB codes were written independently of other implementations such as CMBFAST, CAMB, etc. The library is structured in such way to simplify the inclusion of non-standard cosmological models. Besides the functions related to cosmological quantities, this library also implements mathematical and statistical tools. The former was developed to enable the inclusion of other probes and/or theoretical models and to optimize the codes. The statistical framework comprises algorithms which define likelihood functions, minimization, Monte Carlo, Fisher Matrix and profile likelihood methods.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- M. Aguena, C. Avestruz, C. Combet, S. Fu, R. Herbonnet, A. I. Malz, M. Penna-Lima, M. Ricci, S. D. P. Vitenti, L. Baumont, H. Fan, M. Fong, M. Ho, M. Kirby, C. Payerne, D. Boutigny, B. Lee, B. Liu, T. McClintock, H. Miyatake, C. Sifón, A. von der Linden, H. Wu, M. Yoon: CLMM: a LSST-DESC Cluster weak Lensing Mass Modeling library for cosmology (2021) arXiv
- Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
- E. E. O. Ishida, S. D. P. Vitenti, M. Penna-Lima, J. Cisewski, R. S. de Souza, A. M. M. Trindade, E. Cameron, V. C. Busti, for the COIN collaboration: cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation (2015) arXiv