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Interpreting the distance correlation results for the COMBO-17 survey

The accurate classification of galaxies in large-sample astrophysical databases of galaxy clusters depends sensitively on the ability to distinguish between morphological types, especially at higher redshifts. This capability can be enhanced through as new statistical measure of association and corr...

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Bibliographic Details
Main Authors: Richards, Mercedes (Author) , Richards, Donald (Author) , Martínez-Gómez, Elizabeth (Author)
Format: Article (Journal)
Language:English
Published: 2014 March 19
In: The astrophysical journal. Part 2, Letters
Year: 2014, Volume: 784, Issue: 2
ISSN:2041-8213
DOI:10.1088/2041-8205/784/2/L34
Online Access:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1088/2041-8205/784/2/L34
Verlag, lizenzpflichtig, Volltext: https://iopscience.iop.org/article/10.1088/2041-8205/784/2/L34
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Author Notes:Mercedes T. Richards, Donald St.P. Richards, and Elizabeth Martínez-Gómez
Description
Summary:The accurate classification of galaxies in large-sample astrophysical databases of galaxy clusters depends sensitively on the ability to distinguish between morphological types, especially at higher redshifts. This capability can be enhanced through as new statistical measure of association and correlation, called the distance correlation coefficient, which has more statistical power to detect associations than does the classical Pearson measure of linear relationships between two variables. The distance correlation measure offers a more precise alternative to the classical measure since it is capable of detecting nonlinear relationships that may appear in astrophysical applications. We showed recently that the comparison between the distance and Pearson correlation coefficients can be used effectively to isolate potential outliers in various galaxy data sets, and this comparison has the ability to confirm the level of accuracy associated with the data. In this work, we elucidate the advantages of distance correlation when applied to large databases.
Item Description:Gesehen am 08.09.2020
Physical Description:Online Resource
ISSN:2041-8213
DOI:10.1088/2041-8205/784/2/L34

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