Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Kolmogorov-Smirnov test for the CMB
105
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We investigate the statistics of the cosmic microwave background using the Kolmogorov-Smirnov test. We show that, when we correctly de-correlate the data, the partition function of the Kolmogorov stochasticity parameter is compatible with the Kolmogorov distribution and, contrary to previous claims, the CMB data are compatible with Gaussian fluctuations with the correlation function given by standard Lambda-CDM. We then use the Kolmogorov-Smirnov test to derive upper bounds on residual point source power in the CMB, and indicate the promise of this statistics for further datasets, especially Planck, to search for deviations from Gaussianity and for detecting point sources and Galactic foregrounds.
rate research
Read More
In arxiv:1108.5354 the Kolmogorov-Smirnov (K-S) test and Kolmogorov stochasticity parameter (KSP) is applied to CMB data. Their interpretation of the KSP method, however, lacks essential elements. In addition, their main result on the Gaussianity of CMB was not a matter of debate in previous KSP-CMB studies which also included predictions on cold spots, point sources.
This short note is concerned with a recent paper by N{ae}ss [arXiv:1105.5051]. We explain why the statements in the paper are absolutely irrelevant.
A new method for the determination of electric signal time-shifts is introduced. As the Kolmogorov-Smirnov test, it is based on the comparison of the cumulative distribution functions of the reference signal with the test signal. This method is very fast and thus well suited for on-line applications. It is robust to noise and its performances in terms of precision are excellent for time-shifts ranging from a fraction to several sample durations. PACS. 29.40.Gx (Tracking and position-sensitive detectors), 29.30.Kv (X- and -ray spectroscopy), 07.50.Qx (Signal processing electronics)
We present a two-dimensional version of the classical one-dimensional Kolmogorov-Smirnov (K-S) test, extending an earlier idea due to Peacock (1983) and an implementation proposed by Fasano & Franceschini (1987). The two-dimensional K-S test is used to optimise the goodness of fit in an iterative source-detection scheme for astronomical images. The method is applied to a ROSAT/HRI x-ray image of the post core-collapse globular cluster NGC 6397 to determine the most probable source distribution in the cluster core. Comparisons to other widely-used source detection methods, and to a Chandra image of the same field, show that our iteration scheme is superior in measuring statistics-limited sources in severely crowded fields.
We review a finite-sampling exponential bound due to Serfling and discuss related exponential bounds for the hypergeometric distribution. We then discuss how such bounds motivate some new results for two-sample empirical processes. Our development complements recent results by Wei and Dudley (2011) concerning exponential bounds for two-sided Kolmogorov - Smirnov statistics by giving corresponding results for one-sided statistics with emphasis on adjusted inequalities of the type proved originally by Dvoretzky, Kiefer, and Wolfowitz (1956) and by Massart (1990) for one-samp
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
