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Register a new userBeyondPlanck VI. Noise characterization and modelling
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Added by
H{\\aa}vard Tveit Ihle
Publication date
2020
fields
Physics
and research's language is
English
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We present a Bayesian method for estimating instrumental noise parameters and propagating noise uncertainties within the global BeyondPlanck Gibbs sampling framework, and apply this to Planck LFI time-ordered data. Following previous literature, we adopt a simple $1/f$ model for the noise power spectral density (PSD), and implement an optimal Wiener-filter (or constrained realization) gap-filling procedure to account for masked data. We then use this procedure to both estimate the gapless correlated noise in the time-domain, $n_mathrm{corr}$, and to sample the noise PSD spectral parameters, $xi^n = {sigma_0, f_mathrm{knee}, alpha}$. In contrast to previous Planck analyses, we only assume piecewise stationary noise within each pointing period (PID), not throughout the full mission, but we adopt the LFI DPC results as priors on $alpha$ and $f_mathrm{knee}$. On average, we find best-fit correlated noise parameters that are mostly consistent with previous results, with a few notable exceptions. However, a detailed inspection of the time-dependent results reveals many important findings. First and foremost, we find strong evidence for statistically significant temporal variations in all noise PSD parameters, many of which are directly correlated with satellite housekeeping data. Second, while the simple $1/f$ model appears to be an excellent fit for the LFI 70 GHz channel, there is evidence for additional correlated noise not described by a $1/f$ model in the 30 and 44 GHz channels, including within the primary science frequency range of 0.1-1 Hz. In general, most 30 and 44 GHz channels exhibit excess noise at the 2-$3 sigma$ level in each one hour pointing period. For some periods of time, we also find evidence of strong common mode noise fluctuations across the entire focal plane. (Abridged.)
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This paper describes the mapmaking procedure applied to Planck LFI (Low Frequency Instrument) data. The mapmaking step takes as input the calibrated timelines and pointing information. The main products are sky maps of $I,Q$, and $U$ Stokes components. For the first time, we present polarization maps at LFI frequencies. The mapmaking algorithm is based on a destriping technique, enhanced with a noise prior. The Galactic region is masked to reduce errors arising from bandpass mismatch and high signal gradients. We apply horn-uniform radiometer weights to reduce effects of beam shape mismatch. The algorithm is the same as used for the 2013 release, apart from small changes in parameter settings. We validate the procedure through simulations. Special emphasis is put on the control of systematics, which is particularly important for accurate polarization analysis. We also produce low-resoluti
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We constrain polarized foreground emission between 30 and 70GHz with the Planck Low Frequency Instrument (LFI) and WMAP data within the framework of BeyondPlanck global Bayesian analysis. We combine for the first time full-resolution Planck LFI time-ordered data with low-resolution WMAP sky maps at 33, 40 and 61GHz. Spectral parameters are fit with a likelihood defined at the native resolution of each frequency channel. This analysis represents the first implementation of true multi-resolution component separation applied to CMB observations for both amplitude and spectral energy distribution (SED) parameters. For synchrotron emission, we approximate the SED as a power-law in frequency and find that the low signal-to-noise ratio of the data set strongly limits the number of free parameters that may be robustly constrained. We partition the sky into four large disjoint regions (High Latitude; Galactic Spur; Galactic Plane; and Galactic Center), each associated with its own power-law index. We find that the High Latitude region is prior-dominated, while the Galactic Center region is contaminated by residual instrumental systematics. The two remaining regions appear to be both signal-dominated and clean of systematics, and for these we derive spectral indices of $beta_{mathrm s}^{mathrm{Spur}}=-3.15pm0.07$ and $beta_{mathrm s}^{mathrm{Plane}}=-3.12pm0.06$. This agrees qualitatively with the WMAP-only polarization constraints presented by Dunkley et al. (2009), but contrasts with several temperature-based analyses. For thermal dust emission we assume a modified blackbody model and we fit the power-law index across the full sky. We find $beta_{mathrm{d}}=1.62pm0.04$, which is slightly steeper than that previously reported from Planck HFI data, but still statistically consistent at a 2$sigma$ confidence level.
We describe the processing of the 531 billion raw data samples from the High Frequency Instrument (hereafter HFI), which we performed to produce six temperature maps from the first 473 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 9.7 to 4.6 arcmin. The detector noise per (effective) beam solid angle is respectively, 10, 6, 12 and 39 microKelvin in HFI four lowest frequency channel (100--353 GHz) and 13 and 14 kJy/sr for the 545 and 857 GHz channels. Using the 143 GHz channel as a reference, these two high frequency channels are intercalibrated within 5% and the 353 GHz relative calibration is at the percent level. The 100 and 217 GHz channels, which together with the 143 GHz channel determine the high-multipole part of the CMB power spectrum (50 < l <2500), are intercalibrated at better than 0.2 %.
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