期刊
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF AUSTRALIA
卷 32, 期 -, 页码 -出版社
CAMBRIDGE UNIV PRESS
DOI: 10.1017/pasa.2015.33
关键词
fitting; statistics
Astronomical data is often uncertain with errors that are heteroscedastic (different for each data point) and covariant between different dimensions. Assuming that a set of D-dimensional data points can be described by a (D - 1)dimensional plane with intrinsic scatter, we derive the general likelihood function to be maximised to recover the best fitting model. Alongside the mathematical description, we also release the HYPER-FIT package for the R statistical language (github.com/asgr/hyper.fit) and a user-friendly web interface for online fitting (hyperfit.icrar.org). The HYPER-FIT package offers access to a large number of fitting routines, includes visualisation tools, and is fully documented in an extensive user manual. Most of the HYPER-FIT functionality is accessible via the web interface. In this paper, we include applications to toy examples and to real astronomical data from the literature: the mass-size, Tully-Fisher, Fundamental Plane, and mass-spin-morphology relations. In most cases, the HYPER-FIT solutions are in good agreement with published values, but uncover more information regarding the fitted model.
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