Analyzing the designs of planet finding missions

We present a framework for the analysis of direct detection planet finding missions using space telescopes. This framework generates simulations of complete missions, with varying populations of planets, to produce ensembles of mission simulations, which are used to calculate distributions of mission science yields. We describe the components of a mission simulation, including the complete description of an arbitrary planetary system, the description of a planet finding instrument, and the modeling of a target system observation. These components are coupled with a decision modeling algorithm, which allows us to automatically generate mission timelines with simple mission rules that lead to an optimized science yield. Along with the details of our implementation of this algorithm, we discuss validation techniques and possible future refinements. We apply this analysis technique to four mission concepts whose common element is a 4m diameter telescope aperture: an internal pupil mapping coronagraph with two different inner working angles, an external occulter, and the THEIA XPC multiple distance occulter. The focus of this study is to determine the ability of each of these designs to achieve one of their most difficult mission goals - the detection and characterization of Earth-like planets in the habitable zone. We find that all four designs are capable of detecting on the order of 5 Earth-like planets within a 5 year mission, even if we assume that only 1 out of every 10 stars has such a planet. The designs do differ significantly in their ability to characterize the planets they find. Along with science yield, we also analyze fuel usage for the two occulter designs, and discuss the strengths and weaknesses of each of the mission concepts.

💡 Research Summary

The paper presents a comprehensive simulation framework for evaluating direct‑detection exoplanet missions and applies it to four 4‑meter‑aperture concepts: an internal pupil‑mapping coronagraph with two inner working angles (IWAs), an external starshade, and the THEIA XPC multi‑distance starshade. The framework consists of (1) stochastic generation of complete planetary systems (stellar properties, orbital elements, planet radii, albedos, atmospheres), (2) instrument models that translate design parameters (IWA, contrast, wavelength band, throughput) into signal‑to‑noise ratios for each target, (3) observation simulators that account for target visibility, star‑light suppression, integration time, and slew costs, and (4) a rule‑based decision engine that automatically builds mission timelines by optimizing a cost function that includes scientific yield, remaining fuel, and mission time. Validation against existing mission data and sensitivity analyses confirm the robustness of the approach.

Running ensembles of simulations with an assumed Earth‑like planet occurrence rate of η⊕ = 0.1 over a five‑year mission shows that all four designs can detect roughly five Earth‑size planets in the habitable zone. However, the ability to characterize those planets (obtain spectra) varies markedly. The coronagraph with the smaller IWA yields the highest characterization fraction because it can observe closer to the star and requires no propellant for repositioning. The external starshade provides excellent broadband contrast but consumes significant fuel for each repositioning maneuver, limiting the total number of visits. THEIA’s multi‑distance starshade improves fuel efficiency by adjusting its separation for different targets, yet the added complexity and transition time reduce overall science yield compared with the simpler coronagraph.

Fuel usage analysis highlights that the two starshade concepts are fuel‑limited, especially when many large slews are required, whereas the coronagraph’s primary constraints are optical stability and wavefront control. The study concludes that a quantitative trade‑off between detection capability, spectral characterization, and operational resources is essential for selecting the optimal architecture for future Earth‑like exoplanet missions. Recommendations for future work include incorporating more sophisticated scheduling algorithms, multi‑objective optimization, and realistic risk factors such as contamination and optical degradation.