Iterative destriping and photometric calibration forPlanck-HFI, polarized, multi-detector map-making

Abstract

We present an iterative scheme designed to recover calibrated I, Q, and U maps from Planck-HFI data using the orbital dipole due to the satellite motion with respect to the Solar System frame. It combines a map reconstruction, based on a destriping technique, juxtaposed with an absolute calibration algorithm. We evaluate systematic and statistical uncertainties incurred during both these steps with the help of realistic, Planck-like simulations containing CMB, foreground components and instrumental noise, and assess the accuracy of the sky map reconstruction by considering the maps of the residuals and their spectra. In particular, we discuss destriping residuals for polarization sensitive detectors similar to those of Planck-HFI under different noise hypotheses and show that these residuals are negligible (for intensity maps) or smaller than the white noise level (for Q and U Stokes maps), for > 50. We also demonstrate that the combined level of residuals of this scheme remains comparable to those of the destriping-only case except at very low where residuals from the calibration appear. For all the considered noise hypotheses, the relative calibration precision is on the order of a few 10 −4 , with a systematic bias of the same order of magnitude.

Planck2015 results

“…These levels for the CMB channels are within a factor of 3 of the accuracy floor set by noise in the low-polarization (Tristram et al 2011).…”

“…The calibration and mapmaking operations use this intermediate product as an input. For each detector, the TOIs are only modified by a single offset value per ring, determined using the destriping method described in Tristram et al (2011). The offsets are computed simultaneously for all bolometers at a given frequency, using the full mission data.…”

Optimal cosmic microwave background map-making in the presence of cross-correlated noise

“…Once the data are calibrated, any artifact is flagged out and the pointing is reconstructed, the first step in the CMB data analysis is the estimation of optimal sky maps from the TOD. In the literature two main strategies are present: a maximum-likelihood (minimumvariance) approach (see e.g., de Gasperis et al 2005, and references therein) and destriping techniques (see e.g., Tristram et al 2011, and references therein).…”

Planck2013 results. VI. High Frequency Instrument data processing

“…From the above equation, the offsets o are derived through maximum likelihood, imposing an additional constraint, in our case that the sum of the offsets has to be equal to zero (arbitrarily, since we do not measure the absolute temperature on the sky, only differences). The performance of this implementation has been evaluated using simulations in Tristram et al (2011). We produce temperature and polarization maps for a number of data sets:…”

“…The presence of residual nonlinear systematic biases in our data precludes the use of potentially more precise techniques, such as those discussed in Tristram et al (2011) using the orbital CMB dipole anisotropy. In Planck Collaboration VIII (2014) we show that using a calibration derived from the orbital dipole would lead to larger detector-to-detector relative calibration dispersion, which induces large-angular-scale patterns in polarization through dipole leakage.…”