2020
DOI: 10.3389/fphy.2020.00015
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Likelihood Methods for CMB Experiments

Abstract: A great deal of experimental effort is currently being devoted to the precise measurements of the cosmic microwave background (CMB) sky in temperature and polarisation. Satellites, balloon-borne, and ground-based experiments scrutinize the CMB sky at multiple scales, and therefore enable to investigate not only the evolution of the early Universe, but also its late-time physics with unprecedented accuracy. The pipeline leading from time ordered data as collected by the instrument to the final product is highly… Show more

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Cited by 18 publications
(15 citation statements)
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“…In the present analysis, we follow the same overall strategy, although the lower level of systematics could have allowed a semi-analytical approach (see e.g. Mangilli et al (2015); Vanneste et al (2018); Hamimeche & Lewis (2008); Gerbino et al (2019)) which we leave to future analysis. With this method, we measure τ = 0.0566 +0.0053 −0.0062 at 68% C.L.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the present analysis, we follow the same overall strategy, although the lower level of systematics could have allowed a semi-analytical approach (see e.g. Mangilli et al (2015); Vanneste et al (2018); Hamimeche & Lewis (2008); Gerbino et al (2019)) which we leave to future analysis. With this method, we measure τ = 0.0566 +0.0053 −0.0062 at 68% C.L.…”
Section: Discussionmentioning
confidence: 99%
“…For details about validation and performances of the likelihood approximation seeGerbino et al (2019).5 The likelihood module is built within the clik infrastructure(Planck Collaboration XV 2014; Planck Collaboration ES 2013, 2015 and it is available on http://sroll20.ias.u-psud.fr or on https://web.fe.infn.it/∼pagano/low ell datasets/sroll2 6 http://cosmologist.info/cosmomc 7 http://camb.info…”
mentioning
confidence: 99%
“…Moreover, being a real-space dataset, it is suitable for a number of studies that are not accessible for a spectrumbased likelihood (see, e.g., Planck Collaboration XXIII 2014;Planck Collaboration XVI 2016) and it is capable of exploring non-rotationally invariant cosmologies. For an exhaustive review of likelihood methods, see Gerbino et al (2020).…”
Section: Introductionmentioning
confidence: 99%
“…In particular, an entire slew of very diverse methods have been designed to produce estimates of the temperature or polarization power spectra or estimates of the cosmological parameters from a set of noisy maps of the sky. We can broadly classify these methods as pseudo-C l approaches, [1], [2], [3], [4], [5], see [6] for a review, maximum likelihood methods [7], [8] and Bayesian approaches using Monte-Carlo methods like Metropolis-Hastings [9], [10], [11], [12], Hamiltonian Monte-Carlo [13], [14], or Gibbs sampling [15], [16], [17], [18], [19]. Out of those, the pseudo-C l methods are very quick but require a careful design of a pseudo-likelihood for the data set of interest [6].…”
Section: Introductionmentioning
confidence: 99%
“…We can broadly classify these methods as pseudo-C l approaches, [1], [2], [3], [4], [5], see [6] for a review, maximum likelihood methods [7], [8] and Bayesian approaches using Monte-Carlo methods like Metropolis-Hastings [9], [10], [11], [12], Hamiltonian Monte-Carlo [13], [14], or Gibbs sampling [15], [16], [17], [18], [19]. Out of those, the pseudo-C l methods are very quick but require a careful design of a pseudo-likelihood for the data set of interest [6]. Monte-Carlo sampling techniques avoid this problem but are computationally demanding, in terms of both the time needed to draw a single sample and the number of required samples are concerned.…”
Section: Introductionmentioning
confidence: 99%