We have compared the cosmic microwave background (CMB) temperature anisotropy maps made from one-year time ordered data (TOD) streams that simulated observations of the originally planned 100 GHz Planck Low Frequency Instrument (LFI). The maps were made with three different codes. Two of these, ROMA and MapCUMBA, were implementations of maximum-likelihood (ML) map-making, whereas the third was an implementation of the destriping algorithm. The purpose of this paper is to compare these two methods, ML and destriping, in terms of the maps they produce and the angular power spectrum estimates derived from these maps. The difference in the maps produced by the two ML codes was found to be negligible. As expected, ML was found to produce maps with lower residual noise than destriping. In addition to residual noise, the maps also contain an error which is due to the effect of subpixel structure in the signal on the map-making method. This error is larger for ML than for destriping. If this error is not corrected a bias will be introduced in the power spectrum estimates. This study is related to Planck activities.
Comparison of map-making algorithms for CMB experiments
NATOLI, Paolo;
2006
Abstract
We have compared the cosmic microwave background (CMB) temperature anisotropy maps made from one-year time ordered data (TOD) streams that simulated observations of the originally planned 100 GHz Planck Low Frequency Instrument (LFI). The maps were made with three different codes. Two of these, ROMA and MapCUMBA, were implementations of maximum-likelihood (ML) map-making, whereas the third was an implementation of the destriping algorithm. The purpose of this paper is to compare these two methods, ML and destriping, in terms of the maps they produce and the angular power spectrum estimates derived from these maps. The difference in the maps produced by the two ML codes was found to be negligible. As expected, ML was found to produce maps with lower residual noise than destriping. In addition to residual noise, the maps also contain an error which is due to the effect of subpixel structure in the signal on the map-making method. This error is larger for ML than for destriping. If this error is not corrected a bias will be introduced in the power spectrum estimates. This study is related to Planck activities.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
