The Spectral Correlation Function: A New Tool for Analyzing Spectral Line Maps

Abstract

The correlation 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 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.