Faster Exo-Earth yield for HabEx and LUVOIR via extreme precision radial velocity prior knowledge

The HabEx and LUVOIR mission concepts reported science yields for mission scenarios in which the instruments must search for potentially habitable planets, determine their orbits, and, if worthwhile, invest the integration time for a spectral characterization. We evaluate the impact of prior knowledge on yield for starshade only, coronagraph only, and hybrid architectures. We use perfect prior knowledge to establish an upper bound on yield and use partial prior knowledge from a potential future Extreme Precision Radial Velocity instrument with 3 cm/s sensitivity. We detail a modeling framework that performs dynamically responsive observation scheduling with realistic mission constraints. We evaluate exo-earth yields against three metrics of spectral characterization for four mission architectures and three levels of prior knowledge (none, partial and perfect). The EPRV provided prior knowledge increases yields by $\sim$30\% and accelerates by a factor of 3 - 6 the time to achieve half of the yield of the mission. Prior knowledge makes all the mission architectures more nimble and powerful, and most especially starshade-based architectures. With prior knowledge, a small telescope with a starshade can achieve comparable yield to a larger telescope with a coronagraph.