Statistics – Computation
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21714417f&link_type=abstract
American Astronomical Society, AAS Meeting #217, #144.17; Bulletin of the American Astronomical Society, Vol. 43, 2011
Statistics
Computation
Scientific paper
We model the origin of the binary star system, NGC 6819-3002-a highly improbable star system that is likely the remnant of a dynamical encounter. The horizontal-branch primary star would have had a large enough radius while in it's giant phase to engulf the orbit of the secondary star, making it very unlikely that these two stars were born together. In order to explore the likelihood that the binary was created via a dynamical exchange interaction we use a scattering experiment algorithm to simulate encounters between a single star and binary system. We use this to investigate the possible initial parameters that could produce the currently observed properties of the system. We incorporate the scattering experiments within a genetic algorithm, which searches over the large parameter space and iteratively selects initial parameters that yield the observed binary. The genetic algorithm gives us the ability to confine the potential parameter space into one of a computationally manageable size. We then perform a more systematic search of the identified region of parameter space in order
to determine the multi-dimensional probability distribution of parameters that can produce
NGC 6819-3002. We then correlate that probability distribution with the distribution of binary and stellar parameters of NGC 6819 in order to determine the likelihood that such a dynamical interaction could have occurred. The result of this process shows that NGC 6819-3002 may indeed have originated through a dynamical exchange interaction. Applying this technique to additional stars and star systems in other clusters will allow us to constrain the impact of dynamical encounters on the formation of anomalous objects like NGC 6819-3002.
We gratefully acknowledge funding from the National Science Foundation under grant AST-0908082.
Finzell Thomas
Geller Aaron
Gosnell Natalie
Mathieu Renaud
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