MADAM - a map-making method for CMB experiments

We present a new map-making method for cosmic microwave background (CMB) measurements. The method is based on the destriping technique, but it also utilizes information about the noise spectrum. The low-frequency component of the instrument noise stream is modelled as a superposition of a set of simple base functions, whose amplitudes are determined by means of maximum-likelihood analysis, involving the covariance matrix of the amplitudes. We present simulation results with 1/f noise and show a reduction in the residual noise with respect to ordinary destriping. This study is related to Planck Low Frequency Instrument (LFI) activities.We present a new map-making method for cosmic microwave background (CMB) measurements. The method is based on the destriping technique, but it also utilizes information about the noise spectrum. The low-frequency component of the instrument noise stream is modelled as a superposition of a set of simple base functions, whose amplitudes are determined by means of maximum-likelihood analysis, involving the covariance matrix of the amplitudes. We present simulation results with 1/f noise and show a reduction in the residual noise with respect to ordinary destriping. This study is related to Planck Low Frequency Instrument (LFI) activities.We present a new map-making method for cosmic microwave background (CMB) measurements. The method is based on the destriping technique, but it also utilizes information about the noise spectrum. The low-frequency component of the instrument noise stream is modelled as a superposition of a set of simple base functions, whose amplitudes are determined by means of maximum-likelihood analysis, involving the covariance matrix of the amplitudes. We present simulation results with 1/f noise and show a reduction in the residual noise with respect to ordinary destriping. This study is related to Planck Low Frequency Instrument (LFI) activities.Peer reviewe

Uncertainty of Radiometer Calibration Loads and Its Impact on Radiometric Measurements

We present an uncertainty analysis of radiometer calibration. The procedure can be used to determine the uncertainty in the nominal brightness temperature of the unknown scene. A total power radiometer requires frequent calibration with known reference loads that are connected to the radiometer. Our analysis includes uncertainties from the radiometer calibration loads and from the connecting network (CN) that is required to multiplex calibration loads and scene to the radiometer input. We show the design and analysis of three calibration loads and how their uncertainties propagate from load terminals to the radiometer calibration plane and to the scene. All three loads, including a cryogenic load, are simple, inexpensive, and show great stability and accuracy. We give an uncertainty calculation example for our three calibration loads and for the CN. We validate our model and the long-term stability of the loads through measurements. The analysis is done at 52 GHz, but the analysis and the construction of the loads are generic and easily scalable to other frequencies.</div

Radiometric Resolution Analysis and a Simulation Model

Total power radiometer has a simple configuration and the best theoretical resolution. Gain fluctuations and calibration errors, however, can induce severe errors in the solved scene brightness temperature. To estimate the overall radiometer performance we present a numerical simulation tool that can be used to determine the radiometric resolution. Our model considers three main components that degrade the radiometric resolution: thermal noise, 1/f noise and calibration errors. These error sources have long been known to exist, but comprehensive models able to account all these effects quantitatively and accurately in a practical manner have been missing. We have developed a radiometer simulation model that is able to produce radiometer signals that incorporate realistic radiometer effects that show up as noise and other errors in the radiometer video signal. Our simulation tool integrates the fundamental radiometer theories numerically and allows the investigation of different calibration schemes and receiver topologies. The model can be used as a guide for design and optimization as well as for verification of the radiometer performance. Moreover, it can be extended to a much larger and more complex radiometer systems allowing better system level performance estimation and optimization with minimal bread-board implementations. The model mimics real radiometer video data and thus the complete data analysis pipeline can be developed and verified before the real video data is available. In this paper, the model has been applied to a total power radiometer operating in the 52 GHz frequency range.</p

Making cosmic microwave background temperature and polarization maps with MADAM

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Destriping CMB temperature and polarization maps

We study destriping as a map-making method for temperature-and-polarization data for cosmic microwave background observations. We present a particular implementation of destriping and study the residual error in output maps, using simulated data corresponding to the 70 GHz channel of the Planck satellite, but assuming idealized detector and beam properties. The relevant residual map is the difference between the output map and a binned map obtained from the signal + white noise part of the data stream. For destriping it can be divided into six components: unmodeled correlated noise, white noise reference baselines, reference baselines of the pixelization noise from the signal, and baseline errors from correlated noise, white noise, and signal. These six components contribute differently to the different angular scales in the maps. We derive analytical results for the first three components. This study is related to Planck LFI activities.We study destriping as a map-making method for temperature-and-polarization data for cosmic microwave background observations. We present a particular implementation of destriping and study the residual error in output maps, using simulated data corresponding to the 70 GHz channel of the Planck satellite, but assuming idealized detector and beam properties. The relevant residual map is the difference between the output map and a binned map obtained from the signal + white noise part of the data stream. For destriping it can be divided into six components: unmodeled correlated noise, white noise reference baselines, reference baselines of the pixelization noise from the signal, and baseline errors from correlated noise, white noise, and signal. These six components contribute differently to the different angular scales in the maps. We derive analytical results for the first three components. This study is related to Planck LFI activities.We study destriping as a map-making method for temperature-and-polarization data for cosmic microwave background observations. We present a particular implementation of destriping and study the residual error in output maps, using simulated data corresponding to the 70 GHz channel of the Planck satellite, but assuming idealized detector and beam properties. The relevant residual map is the difference between the output map and a binned map obtained from the signal + white noise part of the data stream. For destriping it can be divided into six components: unmodeled correlated noise, white noise reference baselines, reference baselines of the pixelization noise from the signal, and baseline errors from correlated noise, white noise, and signal. These six components contribute differently to the different angular scales in the maps. We derive analytical results for the first three components. This study is related to Planck LFI activities.Peer reviewe

Planck early results X : Statistical analysis of Sunyaev-Zeldovich scaling relations for X-ray galaxy clusters

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Planck early results XXVI : Detection with Planck and confirmation by XMM-Newton of PLCK G266.6–27.3, an exceptionally X-ray luminous and massive galaxy cluster at z ~ 1

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Planck early results XIX : All-sky temperature and dust optical depth from Planck and IRAS. Constraints on the “dark gas” in our Galaxy

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Planck early results XIII : Statistical properties of extragalactic radio sources in the Planck Early Release Compact Source Catalogue

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