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DISTANCE CORRELATION METHODS FOR DISCOVERING ASSOCIATIONS IN LARGE ASTROPHYSICAL DATABASES
- Source :
- The Astrophysical Journal. 781:39
- Publication Year :
- 2014
- Publisher :
- American Astronomical Society, 2014.
-
Abstract
- High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between different astrophysical variables; hence an ability to detect and verify associations or correlations between variables is important in astrophysical research. In this paper, we apply a recently defined statistical measure called the distance correlation coefficient which can be used to identify new associations and correlations between astrophysical variables. The distance correlation coefficient applies to variables of any dimension; it can be used to determine smaller sets of variables that provide equivalent astrophysical information; it is zero only when variables are independent; and it is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient. Hence, the distance correlation coefficient provides more information than the Pearson coefficient. We analyze numerous pairs of variables in the COMBO-17 database with the distance correlation method and with the maximal information coefficient. We show that the Pearson coefficient can be estimated with higher accuracy from the corresponding distance correlation coefficient than from the maximal information coefficient. For given values of the Pearson coefficient, the distance correlation method has a greater ability than the maximal information coefficient to resolve astrophysical data into highly concentrated V-shapes, which enhances classification and pattern identification. These results are observed over a range of redshifts beyond the local universe and for galaxies from elliptical to spiral.<br />Comment: 11 pages, 6 figures, 4 tables; Astrophysical Journal, accepted, in press
- Subjects :
- FOS: Computer and information sciences
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
FOS: Physical sciences
Mathematics - Statistics Theory
Machine Learning (stat.ML)
Statistics Theory (math.ST)
Astrophysics::Cosmology and Extragalactic Astrophysics
computer.software_genre
Statistics - Applications
symbols.namesake
Statistics - Machine Learning
FOS: Mathematics
Galaxy formation and evolution
Range (statistics)
Applications (stat.AP)
Galaxy cluster
Physics
Database
Astronomy and Astrophysics
Pearson product-moment correlation coefficient
Galaxy
Distance correlation
Space and Planetary Science
Chandra Deep Field South
symbols
Maximal information coefficient
computer
Astrophysics - Cosmology and Nongalactic Astrophysics
Subjects
Details
- ISSN :
- 15384357 and 0004637X
- Volume :
- 781
- Database :
- OpenAIRE
- Journal :
- The Astrophysical Journal
- Accession number :
- edsair.doi.dedup.....ec008f6980ef5b0b7db9d3492c413392
- Full Text :
- https://doi.org/10.1088/0004-637x/781/1/39