Modeling high-dimensional dependence among astronomical data
Roberto Vio, Thomas W. Nagler, Paola Andreani

TL;DR
This paper introduces vine copulas to model high-dimensional dependencies in astronomical data, revealing that far-IR luminosity is a key parameter linking other galaxy properties, with residual relations being negligible after accounting for it.
Contribution
The paper applies vine copulas to high-dimensional astronomical data, overcoming computational and theoretical limitations of traditional methods, and identifies the dominant role of far-IR luminosity.
Findings
Far-IR luminosity is the key parameter linking other galaxy properties.
Residual relations among gas masses and near-IR luminosity are negligible after accounting for far-IR luminosity.
Vine copulas effectively model high-dimensional dependencies in astronomical data.
Abstract
Fixing the relationship of a set of experimental quantities is a fundamental issue in many scientific disciplines. In the 2D case, the classical approach is to compute the linear correlation coefficient from a scatterplot. This method, however, implicitly assumes a linear relationship between the variables. Such an assumption is not always correct. With the use of the partial correlation coefficients, an extension to the multidimensional case is possible. However, the problem of the assumed mutual linear relationship of the variables remains. A relatively recent approach that makes it possible to avoid this problem is the modeling of the joint probability density function (PDF) of the data with copulas. These are functions that contain all the information on the relationship between two random variables. Although in principle this approach also can work with multidimensional data,…
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