Cosmological constant, COBE cosmic microwave background anisotropy, and large-scale clustering

1993 ◽  
Vol 413 ◽  
pp. 1 ◽  
Author(s):  
Lev A. Kofman ◽  
Nickolay Y. Gnedin ◽  
Neta A. Bahcall
2005 ◽  
Vol 201 ◽  
pp. 65-70
Author(s):  
Robert F. Silverberg ◽  

We have developed a balloon-borne experiment to measure the Cosmic Microwave Background Radiation anisotropy on angular scales from ˜50° down to ˜20′. The instrument observes at frequencies between 150 and 690 GHz and will be flown on an Antarctic circumpolar long duration flight. To greatly improve the experiment performance, the front-end of the experiment is mounted on the top of the balloon. With high sensitivity, broad sky coverage, and well-characterized systematic errors, the results of this experiment can be used to strongly constrain cosmological models and probe the early stages of large-scale structure formation in the Universe.


1998 ◽  
Vol 07 (01) ◽  
pp. 89-96 ◽  
Author(s):  
KIN-WANG NG

The effects of limited sky coverage in large-angle cosmic microwave background anisotropy experiments are investigated by computing the variance of the angular two-point correlation function with an incomplete sphere. We find that, assuming a power spectrum of density fluctuations with spectral index n = 1, the Galactic cut of half-width 20° (40°) about the Equator made by the COBE DMR experiment would induce a sample variance on the rms temperature fluctuation [(ΔT/T) rms ]2 (or equivalently, the correlation function at zero lag), which is 12% (38%) greater than the cosmic variance with a whole sky coverage. This result is about two times smaller than the naive expectation that the cosmic variance is enhanced by a factor of [Formula: see text], where A is the solid angle sampled by the experiment. We also find that the sample variance of the correlation function at nonzero lag can approach the cosmic variance limit. Our approach provides an analytic way of finding a theoretical error to the theoretical prediction for a particular experiment (either large- or small-scale), without having recourse to computationally intensive Monte Carlo or maximum likelihood methods.


2007 ◽  
Vol 2007 (02) ◽  
pp. 006-006 ◽  
Author(s):  
Chun-Hsien Wu ◽  
Kin-Wang Ng ◽  
Wolung Lee ◽  
Da-Shin Lee ◽  
Yeo-Yie Charng

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