The hierarchy of correlation functions and its relation to other measures of galaxy clustering

1979 ◽  
Vol 186 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Simon D. M. White
Keyword(s):  
Correlation Functions ◽  
Galaxy Clustering

scholarly journals Anisotropies of galaxy ellipticity correlations in real and redshift space: angular dependence in linear tidal alignment model

2020 ◽  
Vol 493 (1) ◽  
pp. L124-L128 ◽  
Author(s):  
Teppei Okumura ◽  
Atsushi Taruya
Keyword(s):  
Angular Dependence ◽  
Gravitational Lensing ◽  
Correlation Functions ◽  
Clustering Analysis ◽  
Cosmological Parameters ◽  
Line Of Sight ◽  
Acoustic Oscillations ◽  
Galaxy Clustering ◽  
Space Distortion ◽  
Alignment Model

ABSTRACT Investigating intrinsic alignments (IAs) of galaxy shapes is important not only to constrain cosmological parameters unbiasedly from gravitational lensing but also to extract cosmological information complimentary to galaxy clustering analysis. We derive simple and useful formulas for the various IA statistics, including the intrinsic ellipticity–ellipticity correlation, the gravitational shear–intrinsic ellipticity correlation, and the velocity–intrinsic ellipticity correlation functions. The angular dependence of each statistic is explicitly given, namely the angle between the line-of-sight direction and the separation vector of two points. It thus allows us to analyse anisotropies of baryon acoustic oscillations encoded in the IA statistics, and we can extract the maximum cosmological information using the Alcock–Paczynski and redshift-space distortion effects. We also provide these formulas for the intrinsic ellipticities decomposed into E and B modes.

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

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