Abstract
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.
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