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Session 20 - Chemistry & Physical Process in the ISM.
Display session, Wednesday, January 07
Exhibit Hall,

[20.04] Analyzing Molecular Clouds with the Spectral Correlation Function

E. W. Rosolowsky (Swarthmore College), A. A. Goodman, J. P. Williams, D. J. Wilner (SAO)

The Spectral Correlation Function (SCF) is a new data analysis algorithm that measures how the properites of spectra vary from position to position in a spectral-line map. For each spectrum in a data cube, the SCF measures the ``difference" between that spectrum and a specified subset of its neighbors. This algorithm is intended for use on both simulated and observed position-position-velocity data cubes. In initial tests of the SCF, we have shown that a histogram of the SCF for a map is a good descriptor of the spatial-velocity distribution of material. In one test, we compare the SCF distributions for: 1) a real data cube; 2) a cube made from the real cube's spectra with randomized positions; and 3) the results of a preliminary MHD simulation by Gammie, Ostriker, and Stone. The results of the test show that the real cloud and the simulation are much closer to each other in their SCF distributions than is either to the randomized cube. We are now in the process of applying the SCF to a larger set of observed and simulated data cubes. Our ultimate aim is to use the SCF both on its own, as a descriptor of the spatial-kinetic properties of interstellar gas, and also as a tool for evaluating how well simulations resemble observations. Our expectation is that the SCF will be more discriminatory (less likely to produce a false match) than the data cube descriptors currently available.

Program listing for Wednesday