Making sky maps from Planck data

Ashdown, M.; Baccigalupi, C.; Balbi, A.; Bartlett, J. G.; Borrill, J.; Cantalupo, C. M.; Gasperis, G. de; Górski, K. M.; Hivon, E.; Keihänen, E.; Kurki-Suonio, H.; Lawrence, C. R.; Natoli, P.; Poutanen, T.; Prunet, S.; Reinecke, M.; Stompor, R.; Wandelt, B. D.

doi:10.1051/0004-6361:20065829

Cited by 55 publications

(91 citation statements)

References 21 publications

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“…Springtide (Ashdown et al 2007b) is an implementation of the conventional destriping approach which solves for one baseline per pointing period. Since the baselines are so long, it allows for a number of optimizations in the handling of the data.…”

Section: Conventional Destripingmentioning

confidence: 99%

Residual noise covariance forPlancklow-resolution data analysis

Keskitalo

Ashdown

Cabella

et al. 2010

A&A

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Aims. We develop and validate tools for estimating residual noise covariance in Planck frequency maps, we also quantify signal error effects and compare different techniques to produce low-resolution maps. Methods. We derived analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of mapmaking approaches. We tested these analytical predictions using both Monte Carlo simulations and by applying them to angular power spectrum estimation. We used simulations to quantify the level of signal errors incurred in the different resolution downgrading schemes considered in this work. Results. We find excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping mapmakers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. Signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the subpercent level at multipoles, > 2N side , where N side is the HEALPix resolution parameter. We show that, for a polarization measurement, reliable characterization of the residual noise is required to draw reliable constraints on large-scale anisotropy.Conclusions. Methods presented and tested in this paper allow for production of low-resolution maps with both controlled sky signal error level and a reliable estimate of covariance of the residual noise. We have also presented a method for smoothing the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth-limited maps.

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Section: Conventional Destripingmentioning

confidence: 99%

Residual noise covariance forPlancklow-resolution data analysis

Keskitalo

Ashdown

Cabella

et al. 2010

A&A

Self Cite

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“…VI. Maps were also produced with Springtide (Ashdown et al 2007) and madam (Keihänen et al 2005) and lead to consistent results.…”

Section: A6 Page 24 Of 47mentioning

confidence: 79%

“…They are very close to optimal when certain conditions are satisfied (see, for example, Delabrouille 1998; Revenu et al 2000;Maino et al 2002;Keihänen et al 2004;Ashdown et al 2007) In the destriping approach, the noise is divided into a lowfrequency component represented by the offsets o, unfolded onto the time-ordered data by the matrix Γ, and a white noise part n which is uncorrelated with the low-frequency noise. The signal part in the TOI is given by the projection of a pixelised sky map (or set of maps containing the 3 Stokes parameters, arranged as a single vector), T via a pointing matrix, A, leading to…”

Section: Destripingmentioning

confidence: 99%

Planckearly results. VI. The High Frequency Instrument data processing

Team¹,

Ade

Aghanim

et al. 2011

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We describe the processing of the 336 billion raw data samples from the High Frequency Instrument (HFI) which we performed to produce six temperature maps from the first 295 days of Planck-HFI survey data. These maps provide an accurate rendition of the sky emission at 100, 143, 217, 353, 545 and 857 GHz with an angular resolution ranging from 9.9 to 4.4 . The white noise level is around 1.5 μK degree or less in the 3 main CMB channels (100-217 GHz). The photometric accuracy is better than 2% at frequencies between 100 and 353 GHz and around 7% at the two highest frequencies. The maps created by the HFI Data Processing Centre reach our goals in terms of sensitivity, resolution, and photometric accuracy. They are already sufficiently accurate and well-characterised to allow scientific analyses which are presented in an accompanying series of early papers. At this stage, HFI data appears to be of high quality and we expect that with further refinements of the data processing we should be able to achieve, or exceed, the science goals of the Planck project.

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“…of a "destriping" mapmaking code which removes almost all of the effects of 1/ f noise (Poutanen et al 2006;Ashdown et al 2007aAshdown et al ,b, 2009. We use realistic Grasp beams and the parametric model of the reconstructed beams (the results with non-parametric model will be presented in a future paper).…”

Section: Beam Fits and Transfer Function Ensemblesmentioning

confidence: 99%

Markov chain beam randomization: a study of the impact of PLANCK beam measurement errors on cosmological parameter estimation

Rocha

Pagano

Górski

et al. 2010

A&A

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We introduce a new method to propagate uncertainties in the beam shapes used to measure the cosmic microwave background to cosmological parameters determined from those measurements. The method, called markov chain beam randomization (MCBR), randomly samples from a set of templates or functions that describe the beam uncertainties. The method is much faster than direct numerical integration over systematic "nuisance" parameters, and is not restricted to simple, idealized cases as is analytic marginalization. It does not assume the data are normally distributed, and does not require Gaussian priors on the specific systematic uncertainties. We show that MCBR properly accounts for and provides the marginalized errors of the parameters. The method can be generalized and used to propagate any systematic uncertainties for which a set of templates is available. We apply the method to the Planck satellite, and consider future experiments. Beam measurement errors should have a small effect on cosmological parameters as long as the beam fitting is performed after removal of 1/ f noise.

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Making sky maps from Planck data

Cited by 55 publications

References 21 publications

Residual noise covariance forPlancklow-resolution data analysis

Residual noise covariance forPlancklow-resolution data analysis

Planckearly results. VI. The High Frequency Instrument data processing

Markov chain beam randomization: a study of the impact of PLANCK beam measurement errors on cosmological parameter estimation

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