Making sky maps from Planck data

¹ Astrophysics Group, Cavendish Laboratory, J. J. Thomson Avenue, Cambridge CB3 0HE, UK e-mail: maja1@mrao.cam.ac.uk
² Institute of Astronomy, Madingley Road, Cambridge CB3 0HA, UK
³ Institut für Theoretische Astrophysik, Universität Heidelberg, Albert-Überle-Str. 2, 69120 Heidelberg, Germany
⁴ SISSA/ISAS, Via Beirut 4, 34014 Trieste, and INFN, Sezione di Trieste, Via Valerio 2, 34127, Italy
⁵ Dipartimento di Fisica, Università di Roma “Tor Vergata”, via della Ricerca Scientifica 1, 00133 Roma, Italy
⁶ Laboratoire Astroparticule & Cosmologie, 11 place Marcelin Berthelot, 75231 Paris Cedex 05, France (UMR 7164 CNRS, Université Paris 7, CEA, Observatoire de Paris)
⁷ Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
⁸ Space Sciences Laboratory, University of California Berkeley, Berkeley CA 94720, USA
⁹ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena CA 91109, USA
¹⁰ California Institute of Technology, Pasadena CA 91125, USA
¹¹ Warsaw University Observatory, Aleje Ujazdowskie 4, 00478 Warszawa, Poland
¹² Institut d'Astrophysique de Paris, 98 bis boulevard Arago, 75014 Paris, France
¹³ University of Helsinki, Department of Physical Sciences, PO Box 64, 00014 Helsinki, Finland
¹⁴ Helsinki Institute of Physics, PO Box 64, 00014 Helsinki, Finland
¹⁵ Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85741 Garching, Germany
¹⁶ Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana IL 61801, USA
¹⁷ Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana IL 61801, USA

Received: 14 June 2006
Accepted: 9 February 2007

Abstract

Aims.We compare the performance of multiple codes written by different groups for making polarized maps from Planck-sized, all-sky cosmic microwave background (CMB) data. Three of the codes are based on a destriping algorithm; the other three are implementations of an optimal maximum-likelihood algorithm.

Methods.Time-ordered data (TOD) were simulated using the Planck Level-S simulation pipeline. Several cases of temperature-only data were run to test that the codes could handle large datasets, and to explore effects such as the precision of the pointing data. Based on these preliminary results, TOD were generated for a set of four 217 GHz detectors (the minimum number required to produce I, Q, and U maps) under two different scanning strategies, with and without noise.

Results.Following correction of various problems revealed by the early simulation, all codes were able to handle the large data volume that Planck will produce. Differences in maps produced are small but noticeable; differences in computing resources are large.