2011 Aerospace Conference 2011
DOI: 10.1109/aero.2011.5747419
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Creating optimal observing schedules for a starshade planet-finding mission

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Cited by 7 publications
(17 citation statements)
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“…Therefore, instead of interrupting the path and replanning each time we do a characterization observation (as we would in an operational scenario), each path is generated from start to finish with the characterizations included. This simplification was shown by Glassman et al (2011) to provide a close approximation to the results of the full real-time simulation. We ran the simulation for all nine combinations of η ⊕ (0.1, 0.5, or 1) and ε (1, 10, or 100) to show how the mission is affected by the various astrophysical scenarios.…”
Section: Mission Performance: Real-time Schedulingmentioning
confidence: 99%
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“…Therefore, instead of interrupting the path and replanning each time we do a characterization observation (as we would in an operational scenario), each path is generated from start to finish with the characterizations included. This simplification was shown by Glassman et al (2011) to provide a close approximation to the results of the full real-time simulation. We ran the simulation for all nine combinations of η ⊕ (0.1, 0.5, or 1) and ε (1, 10, or 100) to show how the mission is affected by the various astrophysical scenarios.…”
Section: Mission Performance: Real-time Schedulingmentioning
confidence: 99%
“…In the simulated results generated here, the paths can also be sorted by the number of planets detected or characterized, or by the telescope time used, or by the total ΔV consumed for retargeting (8000 m s À1 limit) and for station-keeping (300 m s À1 limit) in order to estimate the science return of the mission. Glassman et al (2011) found that the one path yielding the best science return (in terms of the number of planets detected and characterized) did not generally correspond to a path with high efficiency in terms of ΔV limits and telescope time used. However, they did find that there are many paths near the 90th-percentile science yield that also have very high ΔV efficiencies (∼10th percentile in terms of ΔV cost).…”
Section: Choosing the Best Science Pathmentioning
confidence: 99%
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