Date of Award

1998

Document Type

Restricted Thesis

Terms of Use

© 1998 Erik W. Rosolowsky. All rights reserved. Access to this work is restricted to users within the Swarthmore College network and may only be used for non-commercial, educational, and research purposes. Sharing with users outside of the Swarthmore College network is expressly prohibited. For all other uses, including reproduction and distribution, please contact the copyright holder.

Degree Name

Bachelor of Arts

Department

Physics & Astronomy Department

First Advisor

Alyssa A. Goodman

Second Advisor

David J. Wilner

Abstract

At the current time, the study of the Interstellar Medium (ISM) can be approached from two different angles. The first is by analyzing observational results from radio astronomy, thereby deducing the structure of the ISM. The second method is to model the conditions in the ISM using Magneto-Hydrodynamic (MHD) simulations where the initial conditions and time evolution illuminate the properties of the ISM. The Spectral Correlation Function (SCF) is a new data analysis algorithm that measures how the properties of spectra vary from position to position in a spectral-line map. For each spectrum in a position-position-velocity data cube, the SCF measures the differences in shape and size between that spectrum and a specified subset of its neighbors. This algorithm is intended for use on both simulated and observational data cubes. We have shown that a histogram of the SCF for a map is a good descriptor of the spatial-velocity distribution of material. In this thesis, two observational and two theoretical data sets derived from MHD simulations are analyzed. By studying the SCF distributions for these cubes and the same data in randomized positions, the data indicate that the distributions for all data sets in their original configurations are similar, and these are much different from the data sets with their constituent spectra in randomized positions. The character of these similarities and differences can be used to analyze the data sets. The ultimate aim of the SCF project is to use the SCF both on its own, as a tool for simplifying the analysis of spectra maps, and as a method of evaluating how well simulations resemble observations.

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