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Register a new userPhase analysis of the cosmic microwave background from an incomplete sky coverage
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Phases of the spherical harmonic analysis of full-sky cosmic microwave background (CMB) temperature data contain useful information complementary to the ubiquitous angular power spectrum. In this letter we present a new method of phase analysis on incomplete sky maps. It is based on Fourier phases of equal-latitude pixel rings of the map, which are related to the mean angle of the trigonometric moments from the full-sky phases. They have an advantage for probing regions of interest without tapping polluted Galactic plane area, and can localize non-Gaussian features and departure from statistical isotropy in the CMB.
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The Cosmic Microwave Background (CMB) is an abundant source of cosmological information. However, this information is encoded in non-trivial ways in a signal that is difficult to observe. The resulting challenges in extracting this information from CMB data sets have created a new frontier. In this talk I will discuss the challenges of CMB data analysis. I review what cosmological information is contained in the CMB data and the problem of extracting it. CMB analyses can be divided into two types: ``canonical parameter extraction which seeks to obtain the best possible estimates of cosmological parameters within a pre-defined theory space and hypothesis testing which seeks to test the assumption on which the canonical tests rest. Both of these activities are fundamentally important. In addition to mining the CMB for cosmological information cosmologists would like to strengthen the analysis with data from other cosmologically interesting observations as well as physical constraints. This gives an opportunity 1) to test the results from these separate probes for concordance and 2) if concordance is established to sharpen the constraints on theory space by combining the information from these separate sources.
We present constraints on the number of relativistic species from a joint analysis of cosmic microwave background (CMB) fluctuations and light element abundances (helium and deuterium) compared to big bang nucleosynthesis (BBN) predictions. Our BBN calculations include updates of nuclear rates in light of recent experimental and theoretical information, with the most significant change occuring for the d(p,gamma)^3He cross section. We calculate a likelihood function for BBN theory and observations that accounts for both observational errors and nuclear rate uncertainties and can be easily embedded in cosmological parameter fitting. We then demonstrate that CMB and BBN are in good agreement, suggesting that the number of relativistic species did not change between the time of BBN and the time of recombination. The level of agreement between BBN and CMB, as well as the agreement with the standard model of particle physics, depends somewhat on systematic differences among determinations of the primordial helium abundance. We demonstrate that interesting constraints can be derived combining only CMB and D/H observations with BBN theory, suggesting that an improved D/H constraint would be an extremely valuable probe of cosmology.
In our recent paper (Yadav et al. 2007) we described a fast cubic (bispectrum) estimator of the amplitude of primordial non-Gaussianity of local type, f_{NL}, from a combined analysis of the Cosmic Microwave Background (CMB) temperature and E-polarization observations. In this paper we generalize the estimator to deal with a partial sky coverage as well as inhomogeneous noise. Our generalized estimator is still computationally efficient, scaling as O(N^3/2) compared to the O(N^5/2) scaling of the brute force bispectrum calculation for sky maps with N pixels. Upcoming CMB experiments are expected to yield high-sensitivity temperature and E-polarization data. Our generalized estimator will allow us to optimally utilize the combined CMB temperature and E-polarization information from these realistic experiments, and to constrain primordial non-Gaussianity.
In order to extract cosmological information from observations of the millimeter and submillimeter sky, foreground components must first be removed to produce an estimate of the cosmic microwave background (CMB). We developed a machine-learning approach for doing so for full-sky temperature maps of the millimeter and submillimeter sky. We constructed a Bayesian spherical convolutional neural network architecture to produce a model that captures both spectral and morphological aspects of the foregrounds. Additionally, the model outputs a per-pixel error estimate that incorporates both statistical and model uncertainties. The model was then trained using simulations that incorporated knowledge of these foreground components that was available at the time of the launch of the Planck satellite. On simulated maps, the CMB is recovered with a mean absolute difference of $<4mu$K over the full sky after masking map pixels with a predicted standard error of $>50mu$K; the angular power spectrum is also accurately recovered. Once validated with the simulations, this model was applied to Planck temperature observations from its 70GHz through 857GHz channels to produce a foreground-cleaned CMB map at a Healpix map resolution of NSIDE=512. Furthermore, we demonstrate the utility of the technique for evaluating how well different simulations match observations, particularly in regard to the modeling of thermal dust.
We present Cosmic Microwave Background (CMB) maps from the Santa Barbara HACME balloon experiment (Staren etal 2000), covering about 1150 square degrees split between two regions in the northern sky, near the stars gamma Ursae Minoris and alpha Leonis, respectively. The FWHM of the beam is about 0.77 degrees in three frequency bands centered on 39, 41 and 43 GHz. The results demonstrate that the thoroughly interconnected scan strategy employed allows efficient removal of 1/f-noise and slightly variable scan-synchronous offsets. The maps display no striping, and the noise correlations are found to be virtually isotropic, decaying on an angular scale around one degree. The noise performance of the experiment resulted in an upper limit on CMB anisotropy. However, our results demonstrate that atmospheric contamination and other systematics resulting from the circular scanning strategy can be accurately controlled, and bodes well for the planned follow-up experiments BEAST and ACE, since they show that even with the overly cautious assumption that 1/f-noise and offsets will be as dominant as for HACME, the problems they pose can be readily overcome with the mapmaking algorithm discussed. Our prewhitened notch-filter algorithm for destriping and offset removal should be useful also for other balloon- and ground-based experiments whose scan strategies involve substantial interleaving.
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