POMME is a new estimator that marginalizes signals varying slower than the HWP rotation timescale to produce unbiased CMB polarisation maps with near-optimal noise in the presence of strong contaminants.
MAPCUMBA : a fast iterative multi-grid map-making algorithm for CMB experiments
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abstract
The data analysis of current Cosmic Microwave Background (CMB) experiments like BOOMERanG or MAXIMA poses severe challenges which already stretch the limits of current (super-) computer capabilities, if brute force methods are used. In this paper we present a practical solution to the optimal map making problem which can be used directly for next generation CMB experiments like ARCHEOPS and TopHat, and can probably be extended relatively easily to the full PLANCK case. This solution is based on an iterative multi-grid Jacobi algorithm which is both fast and memory sparing. Indeed, if there are N_tod data points along the one dimensional timeline to analyse, the number of operations is O(N_tod ln N_tod) and the memory requirement is O(N_tod). Timing and accuracy issues have been analysed on simulated ARCHEOPS and TopHat data, and we discuss as well the issue of the joint evaluation of the signal and noise statistical properties.
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Robust CMB polarisation mapmaking with a rotating half-wave plate
POMME is a new estimator that marginalizes signals varying slower than the HWP rotation timescale to produce unbiased CMB polarisation maps with near-optimal noise in the presence of strong contaminants.