The Planck-LFI instrument: Analysis of the $1/f$ noise and
implications for the scanning strategy

Maino, D.; Burigana, C.; Maltoni, Michele; Wandelt, B. D.; Górski, K. M.; Malaspina, M.; Bersanelli, M.; Mandolesi, N.; Banday, A. J.; Hivon, E.

doi:10.1051/aas:1999429

Cited by 73 publications

(101 citation statements)

References 9 publications

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“…The code developed by Burigana et al (1997b) and Maino et al (1999) has been used to generate maps from TODs. With this code it is also possible to significantly reduce correlated systematic effects as the 1/ f α noise stripes from the data before generating the final maps.…”

Section: Simulation Toolsmentioning

confidence: 99%

The effect of signal digitisation in CMB experiments

Maris

Maino

Burigana

et al. 2004

A&A

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Abstract. Signal digitisation may produce significant effects in balloon -borne or space CMB experiments, when the limited bandwidth for downlink of data requires loss-less data compression. In fact, the data compressibility depends on the quantization step q applied on board by the instrument acquisition chain. In this paper we present a study of the impact of the quantization error in CMB experiments using, as a working case, simulated data from the P/LFI 30 and 100 GHz channels. At TOD level, the effect of the quantization can be approximated as a source of nearly normally distributed noise, with RMS q/ √ 12N s , with deviations from normality becoming relevant for a relatively small number of repeated measures N s < ∼ 20. At map level, the data quantization alters the noise distribution and the expectation of some higher order moments. We find a constant ratio, 1/( √ 12σ/q), between the RMS of the quantization noise and RMS of the instrumental noise, σ over the map ( 0.14 for σ/q 2), while, for σ/q ∼ 2, the bias on the expectation for higher order moments is comparable to their sampling variances. Finally, we find that the quantization introduces a power excess, C ex , that, although related to the instrument and mission parameters, is weakly dependent on the multipole at middle and large and can be quite accurately subtracted. For σ/q 2, the residual uncertainty, ∆C ex , implied by this subtraction is only 1-2% of the RMS uncertainty, ∆C noise , on C sky reconstruction due to the noise power, C noise . Only for < ∼ 30 the quantization removal is less accurate; in fact, the 1/ f noise features, although efficiently removed, increase C noise , ∆C noise , C ex and then ∆C ex ; anyway, at low multipoles C sky ∆C noise > ∆C ex . This work is based on P LFI activities.

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Section: Simulation Toolsmentioning

confidence: 99%

The effect of signal digitisation in CMB experiments

Maris

Maino

Burigana

et al. 2004

A&A

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“…More sophisticated methods can be divided into optimal, least squares map-making Tegmark 1997a,b;Borrill et al 2001;Natoli et al 2001;Doré et al 2001), and approximate methods, such as destriping (e.g. Delabrouille 1998;Burigana et al 1999;Maino et al 1999;Revenu et al 2000;Keihänen et al 2004;Kurki-Suonio et al 2009). …”

Section: Map-making and Removal Approachesmentioning

confidence: 99%

Effect of Fourier filters in removing periodic systematic effects from CMB data

Gasperin

Mennella

Maino

et al. 2011

A&A

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We consider the application of high-pass Fourier filters to remove periodic systematic fluctuations from full-sky survey CMB datasets. We compare the filter performance with destriping codes commonly used to remove the effect of residual 1/f noise from timelines. As a realistic working case, we use simulations of the typical Planck scanning strategy and Planck Low Frequency Instrument noise performance, with spurious periodic fluctuations that mimic a typical thermal disturbance. We show that the application of Fourier high-pass filters in chunks always requires subsequent normalisation of induced offsets by means of destriping. For a complex signal containing all the astrophysical and instrumental components, the result obtained by applying filter and destriping in series is comparable to the result obtained by destriping only, which makes the usefulness of Fourier filters questionable for removing this kind of effects.

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“…Specialized data processing algorithms (e.g., Tegmark 1997;Hobson et al 1998;Bouchet and Gispert 1999) will use this information to iteratively extract the signal due to each foreground component, until only the CMB signal remains. Instrumental systematic effects (e.g., Delabrouille 1998B urigana et al 1998Maino et al 1999;Wu et al 2000), as well as uncertainties…”

Section: Scientific Performancementioning

confidence: 99%