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Published July 1, 2008 | Published
Journal Article Open

SANEPIC: a mapmaking method for time stream data from large arrays


We describe a mapmaking method that we have developed for the Balloon-borne Large Aperture Submillimeter Telescope (BLAST) experiment, but which should have general application to data from other submillimeter arrays. Our method uses a maximum likelihood-based approach, with several approximations, which allows images to be constructed using large amounts of data with fairly modest computer memory and processing requirements. This new approach, Signal and Noise Estimation Procedure Including Correlations (SANEPIC), builds on several previous methods but focuses specifically on the regime where there are a large number of detectors sampling the same map of the sky, and explicitly allowing for the possibility of strong correlations between the detector time streams. We provide real and simulated examples of how well this method performs compared with more simplistic mapmakers based on filtering. We discuss two separate implementations of SANEPIC: a brute-force approach, in which the inverse pixel-pixel covariance matrix is computed, and an iterative approach, which is much more efficient for large maps. SANEPIC has been successfully used to produce maps using data from the 2005 BLAST flight.

Additional Information

© 2008 The American Astronomical Society. Received 2007 May 29; accepted 2007 November 27. The BLAST collaboration acknowledges the support of NASA through grants NAG5-12785, NAG5-13301, and NNGO-6GI11G, the Canadian Space Agency (CSA), Canada's Natural Sciences and Engineering Research Council (NSERC), and the UK Particle Physics and Astronomy Research Council (PPARC). We would also like to thank the Columbia Scientific Balloon Facility (CSBF) staff for their outstanding work. We are grateful to Matthew Hasselfield and DuncanHanson for valuable discussions that contributed to the development of SANEPIC. L. O. acknowledges partial support by the Puerto Rico Space Grant Consortium and by the Fondo Istitucional para la Investigacion of the University of Puerto Rico. C. B. N. acknowledges support from the Canadian Institute for Advanced Research. This research made use of WestGrid computing resources, which are funded in part by the Canada Foundation for Innovation, Alberta Innovation and Science, BC Advanced Education, and the participating research institutions.

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