Date of Award

8-31-2017

Document Type

Open Access Thesis

Degree Name

Master of Science (MS)

Department

Physics, Applied

First Advisor

Jonathan Celli

Second Advisor

Bala Sundaram

Third Advisor

Chandra Yelleswarapu

Abstract

Near-Earth Objects (NEOs) are generally small, dark, and fast-moving. Multiple observations over time are necessary to constrain NEO orbits. Orbits based on observational data are inherently uncertain. Here we describe code written in Python and Fortran used to generate synthetic asteroids and compare calculated orbital fit based on noisy ephemeris using the a distance criteria, D-value. Observational sessions separated by more than one month produce very good orbital fits (low D-values) even at the highest noise level. Daily observational sessions show the highest D-values, as expected, since observed points on the orbital ellipse are not well separated. D-value is closely correlated to differences in the eccentricity and inclination of compared orbits.

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