1999/03/31 by Erik W. Rosolowsky, Alyssa A. Goodman, David J. Wilner +1 · 3 citations
Physics and Astronomy · #Astrophysics and Star Formation Studies #Correlation #Correlation function (quantum field theory) #Cube (algebra) #Data cube #Galaxies: Formation, Evolution, Phenomena #Line (geometry) #Measure (data warehouse) #Spectral line #Statistic #Statistical Mechanics and Entropy #astro-ph
paper · pdf · doi:10.1086/307863
29 pages, including 4 figures (tar file submitted as source) See also: http://cfa-www.harvard.edu/~agoodman/scf/velocity_methods.html
arxiv created 1999/04/20 · openalex publication_date 1999/10/20 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06
The "spectral correlation function" analysis we introduce in this paper is a new tool for analyzing spectral line data cubes. Our initial tests, carried out on a suite of observed and simulated data cubes, indicate that the spectral correlation function (SCF) is likely to be a more discriminating statistic than other statistical methods normally applied. The SCF is a measure of similarity between neighboring spectra in the data cube. When the SCF is used to compare a data cube consisting of spectral line observations of the interstellar medium (ISM) with a data cube derived from MHD simulations of molecular clouds, it can find differences that are not found by other analyses. The initial results presented here suggest that the inclusion of self-gravity in numerical simulations is critical for reproducing the correlation behavior of spectra in star-forming molecular clouds.