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Radiometeric CMB measurements need to be highly stable and this stability is best obtained with differential receivers. The residual 1/f noise in the differential output is strongly dependent on the radiometer input offset which can be cancelled using various balancing strategies. In this paper we discuss a software method implemented in the Planck-LFI pseudo-correlation receivers which uses a tunable gain modulation factor, r, in the sky-load difference. Numerical simulations and experimental data show how proper tuning of the parameter r ensures a very stable differential output with knee frequencies of the order of few mHz. Various approaches to calculate r using the radiometer total power data are discussed with some examples relevant to Planck-LFI. Although the paper focuses on pseudo-correlation receivers and the examples are relative to Planck-LFI, the proposed method and its analysis is general and can be applied to a large class of differential radiometric receivers.
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The LFI (Low Frequency Instrument) on board the ESA Planck satellite is constituted by an array of radiometric detectors actively cooled at 20 K in the 30-70 GHz frequency range in the focal plane of the Planck telescope. In this paper we present an overview of the LFI instrument, with a particular focus on the radiometer design. The adopted pseudo-correlation scheme uses a software balancing technique (with a tunable parameter called gain modulation factor) which is effective in reducing the radiometer susceptibility to amplifier instabilities also in presence of small non-idealities in the radiometric chain components, provided that the gain modulation factor is estimated with an accuracy of the order of 0.2%. These results have been recently confirmed by experimental laboratory measurements conducted on the LFI prototype radiometers at 30, 70 and 100 GHz.
Correlation radiometers make true differential measurements in power with high accuracy and small systematic errors. This receiver architecture has been used in radio astronomy for measurements of continuum radiation for over 50 years; this article examines spectroscopy over broad bandwidths using correlation techniques. After general discussions of correlation and the choice of hybrid phase, experimental results from tests with a simple laboratory multi-channel correlation radiometer are shown. Analysis of the effect of the input hybrids phase shows that a 90 degree hybrid is likely to be the best general choice for radio astronomy, depending on its amplitude match and phase flatness with frequency. The laboratory results verify that the combination of the correlation architecture and an analog lag correlator is an excellent method for spectroscopy over very wide bandwidths.
Observation of the fine structures (anisotropies, polarization, spectral distortions) of the Cosmic Microwave Background (CMB) is hampered by instabilities, 1/f noise and asymmetries of the radiometers used to carry on the measurements. Addition of modulation and synchronous detection allows to increase the overall stability and the noise rejection of the radiometers used for CMB studies. In this paper we discuss the advantages this technique has when we try to detect CMB polarization. The behaviour of a two channel correlation receiver to which phase modulation and synchronous detection have been added is examined. Practical formulae for evaluating the improvements are presented.
POLARBEAR-2 (PB-2) is a cosmic microwave background (CMB) polarization experiment that will be located in the Atacama highland in Chile at an altitude of 5200 m. Its science goals are to measure the CMB polarization signals originating from both primordial gravitational waves and weak lensing. PB-2 is designed to measure the tensor to scalar ratio, r, with precision {sigma}(r) < 0.01, and the sum of neutrino masses, {Sigma}m{ u}, with {sigma}({Sigma}m{ u}) < 90 meV. To achieve these goals, PB-2 will employ 7588 transition-edge sensor bolometers at 95 GHz and 150 GHz, which will be operated at the base temperature of 250 mK. Science observations will begin in 2017.
This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price dataset. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.
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