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BeyondPlanck II. CMB map-making through Gibbs sampling | | E. Keihänen
; A.-S. Suur-Uski
; K. J. Andersen
; R. Aurlien
; R. Banerji
; M. Bersanelli
; S. Bertocco
; M. Brilenkov
; M. Carbone
; L. P. L. Colombo
; H. K. Eriksen
; M. K. Foss
; C. Franceschet
; U. Fuskeland
; S. Galeotta
; M. Galloway
; S. Gerakakis
; E. Gjerløw
; B. Hensley
; D. Herman
; M. Iacobellis
; M. Ieronymaki
; H. T. Ihle
; J. B. Jewell
; A. Karakci
; R. Keskitalo
; G. Maggio
; D. Maino
; M. Maris
; A. Mennella
; S. Paradiso
; B. Partridge
; M. Reinecke
; T. L. Svalheim
; D. Tavagnacco
; H. Thommesen
; M. Tomasi
; D. J. Watts
; I. K. Wehus
; A. Zacchei
; | | Date: |
11 Nov 2020 | | Abstract: | We present a Gibbs sampling solution to the map-making problem for CMB
measurements, building on existing destriping methodology. Gibbs sampling
breaks the computationally heavy destriping problem into two separate steps;
noise filtering and map binning. Considered as two separate steps, both are
computationally much cheaper than solving the combined problem. This provides a
huge performance benefit as compared to traditional methods, and allows us for
the first time to bring the destriping baseline length to a single sample. We
apply the Gibbs procedure to simulated Planck 30 GHz data. We find that gaps in
the time-ordered data are handled efficiently by filling them with simulated
noise as part of the Gibbs process. The Gibbs procedure yields a chain of map
samples, from which we may compute the posterior mean as a best-estimate map.
The variation in the chain provides information on the correlated residual
noise, without need to construct a full noise covariance matrix. However, if
only a single maximum-likelihood frequency map estimate is required, we find
that traditional conjugate gradient solvers converge much faster than a Gibbs
sampler in terms of total number of iterations. The conceptual advantages of
the Gibbs sampling approach lies in statistically well-defined error
propagation and systematic error correction, and this methodology forms the
conceptual basis for the map-making algorithm employed in the BeyondPlanck
framework, which implements the first end-to-end Bayesian analysis pipeline for
CMB observations. | | Source: | arXiv, 2011.06024 | | Services: | Forum | Review | PDF | Favorites | |
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