{
  "id": "2011.06024",
  "version": "v1",
  "published": "2020-11-11T19:22:06.000Z",
  "updated": "2020-11-11T19:22:06.000Z",
  "title": "BeyondPlanck II. CMB map-making through Gibbs sampling",
  "authors": [
    "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"
  ],
  "comment": "11 pages, 10 figures. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science during the online release conference (November 18-20, 2020). Connection details will be made available at the same website. Registration is mandatory for the online tutorial, but optional for the conference",
  "categories": [
    "astro-ph.CO"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-11T19:22:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "gibbs sampling",
      "cmb map-making",
      "traditional conjugate gradient solvers converge",
      "beyondplanck",
      "first end-to-end bayesian analysis pipeline"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}