Abstract
We present an analytical model for the desired kinematics of the starshade-telescope relative motion during exoplanet direct imaging observations. We combine this model with an existing deadbanding strategy published by the NASA JPL S5 Team to define a dynamics framework for deadbanding simulations. Global results of these simulations show that the fuel usage and the number of observation interruptions vary as a function of the target star ecliptic coordinates and time, meaning there exist optimal times to observe particular targets. We combine these results with the telescope pointing constraints due to the relative position of the Sun and other bright solar system objects. We show that optimally scheduling an observation could result in up to 30 more min of integration time and 26 fewer interruptions per observation, improvements of almost 300% in some cases. We also show how phasing the start time of the telescope on its halo orbit is paramount for ensuring optimal observations, providing up to 68 additional min and 31 fewer interruptions per observation. Choosing an optimal halo phasing can also increase, for some near-ecliptic target stars, the fraction of a year that the target is observable from a few percent to more than 30%.
Highlights
Creating optimal observation schedules for exoplanet direct imaging with a starshade requires careful consideration of mission constraints
We show the relationship between the keepout zones and halo orbit phasing, presenting an optimal phasing for given target stars that will always lead to optimal observations
We presented a comprehensive analytical model for starshade formation flying as a function of known quantities
Summary
Creating optimal observation schedules for exoplanet direct imaging with a starshade requires careful consideration of mission constraints. We conduct similar simulations of the offset dynamics, first by defining the necessary frames using our analytical starshade model and integrating our equations of relative motion within a Python environment using standard scipy and numpy packages.[15] Our open-source software provides accessibility and the ability to simulate deadbanding of the starshade during observations with a wide set of parameters, including any target star coordinate at any point throughout the mission time and any telescope orbit. The lateral component of the disturbance acceleration varies with the LOS configuration relative to the Sun, Earth, and Moon This causes variation in the fuel use and required number of thruster firings as a function of time (or its position in orbit) and the location of the target star on the sky. We show the relationship between the keepout zones and halo orbit phasing, presenting an optimal phasing for given target stars that will always lead to optimal observations
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