Cosmological parameters from CMB and other data: A Monte Carlo approach

By A. Lewis, Sarah Bridle. 2002

2,823 citations recorded by the source corpus.

We present a fast Markov chain Monte Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent cosmic microwave background ~CMB! experiments and provide parameter constraints, including s 8, from the CMB independent of other data. We next combine data from the CMB, HST Key Project, 2dF galaxy redshift survey, supernovae type Ia and big-bang nucleosynthesis. The Monte Carlo method allows the rapid investigation of a large number of parameters, and we present results from 6 and 9 parameter analyses of flat models, and an 11 parameter analysis of non-flat models. Our results include constraints on the neutrino mass ( mn&0.3 eV), equation of state of the dark energy, and the tensor amplitude, as well as demonstrating the effect of additional parameters on the base parameter constraints. In a series of appendixes we describe the many uses of importance sampling, including computing results from new data and accuracy correction of results generated from an approximate method. We also discuss the different ways of converting parameter samples to parameter constraints, the effect of the prior, assess the goodness of fit and consistency, and describe the use of analytic marginalization over normalization parameters.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/9cb0894f8788218357ca4174236b4c46c3d4d507

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Citations in the corpus

Interacting dark energy with time varying equation of state and the H0 tension (2018, methodology): “In order to extract the observational constraints on the free and derived parameters of the interacting models, we perform a fitting analysis using our modified version of the markov chain monte carlo package cosmomc [66, 67] that is equipped with a convergence diagnostic based on the Gelman and Rub”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: Cosmological implications of the configuration-space clustering wedges (2016, methodology): “We use the 2015 July version of COSMOMC (Lewis & Bridle 2002), which in turn uses CAMB to compute the linear-theory CMB and matter power spectra (Lewis, Challinor & Lasenby 2000), modified to compute the model of non-linearities, bias and RSD described in Section 3.1.”
Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples (2014, methodology): “We follow assumptions from Planck Collaboration XVI (2014), fitting for the baryon density today ωb = Ωbh2, the cold dark matter density today ωc = Ωch2, θMC, the CosmoMC approximation of the sound horizon angular size computed from the Hu & Sugiyam (1996) fitting formulae, τ, the Thomson scattering”
Interacting dark energy in the early 2020s: A promising solution to the H0 and cosmic shear tensions (2019, methodology): “modified version of the publicly available MCMC sampler CosmoMC [285].”
A MEASUREMENT OF THE DAMPING TAIL OF THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM WITH THE SOUTH POLE TELESCOPE (2011, methodology): “We use the CosmoMC31 software package (Lewis & Bridle 2002), which itself uses the CAMB32 software package (Lewis et al. 2000) to calculate the lensed CMB power spectra.”
Nonlinear perturbation theory extension of the Boltzmann code CLASS (2020, methodology): “Building upon years of development starting with CMBFast [2], the two most popular and independently designed Boltzmann solvers that have emerged are CAMB [3] and CLASS [4].”
SNLS3: CONSTRAINTS ON DARK ENERGY COMBINING THE SUPERNOVA LEGACY SURVEY THREE-YEAR DATA WITH OTHER PROBES (2011, methodology): “The third approach is the CosmoMC program (Lewis & Bridle 2002), which uses a Markov-Chain Monte Carlo technique to explore cosmological parameter space.”
Measurements of the E -mode polarization and temperature- E -mode correlation of the CMB from SPT-3G 2018 data (2021, methodology): “We obtain cosmological parameter constraints using the Markov chain Monte Carlo (MCMC) package CosmoMC [57].”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample (2016, methodology): “Satpathy et al. (2016) fit scales between 25 and 150h−1Mpc with bin width of 5h−1Mpc and extract the cosmological and growth parameters with a Markov Chain Monte Carlo (MCMC) algorithm using COSMOMC (Lewis & Bridle 2002).”
Dark Energy Survey Year 1 Results: A Precise H0 Estimate from DES Y1, BAO, and D/H Data (2017, methodology): “…12, 45 Zuntz J. et al., 2017, preprint (arXiv:1708.01533) APPENDI X A : IMPORTANCE SAMPLI NG W I T H N U I S A N C E PA R A M E T E R S The SPTpol likelihood was written as a CosmoMC (Lewis & Bridle 2002) module, whereas the DES likelihood was written as a CosmoSIS (Zuntz et al. 2015) module.”
A METHOD FOR MEASURING (SLOPES OF) THE MASS PROFILES OF DWARF SPHEROIDAL GALAXIES (2011, methodology): “One obtains marginalized probability distributions for any single parameter, or combination of parameters, simply by counting the number of points in the final chain that fall within binned ranges of parameter values (Lewis & Bridle 2002).”
HIGH-RESOLUTION CMB POWER SPECTRUM FROM THE COMPLETE ACBAR DATA SET (2008, methodology): “The pipeline is based on the publicly available CosmoMC 15 package (Lewis & Bridle 2002).”
DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data (2024, methodology): “We perform an MCMC sampling of the parameter space using the Metropolis-Hastings [90, 91] algorithm implemented in the publicly available sampler cobaya [89].”
A MEASUREMENT OF GRAVITATIONAL LENSING OF THE MICROWAVE BACKGROUND USING SOUTH POLE TELESCOPE DATA (2012, methodology): “To explore high-dimensional parameter volumes, we use Monte Carlo Markov Chain (MCMC) techniques (Christensen et al. 2001; Lewis & Bridle 2002).”
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Measuring growth rate and geometry with anisotropic clustering (2013, methodology): “To sample cosmological parameter space, we use the Monte Carlo Markov Chain (MCMC) technique implemented by the COSMOMC package (Lewis & Bridle 2002).”
KiDS+VIKING-450: Cosmic shear tomography with optical and infrared data (2018, methodology): “…two different analysis pipelines, the cosmological inference code CosmoLSS23. that was used for H17 (based on CosmoMC and camb; Lewis et al. 2000; Lewis & Bridle 2002) as well as a pipeline based on CosmoSIS (Zuntz et al. 2015) using the emcee sampler 23 CosmoLSS moreover forms a benchmark of…”