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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.
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
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 e
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 m
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 prim
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)