Maximizing the lessons to be learned from the Roman coronagraph

PI: Macintosh, Bruce, University Of California, Santa Cruz
Coronagraph Community Participation Program

The driving reasons for the existence of the Roman Coronagraph technology demonstrator is as a pathfinder for a future coronagraph with full science capability - now embodied in the Habitable Worlds Observatory. This provides an extraordinary opportunity to learn lessons that will optimize HWO’s scientific goals, design, and (ultimately) operations.

Before launch, the original Formulation Science Working Group team, the coronagraph project team, and now the CPP have developed extensive models of expected performance, ranging from analytic contrast budgets to full-physics STOP and coronagraph models. Similar models are now being used in the design of HWO; long-lead decisions that shape the future of $10B Great Observatory will be made on the bases of these tools.

We propose to work with the project team to ensure that Roman observations yield the most possible information to assess, validate, and refine those models. We will help prioritize target selections based on what each will tell us - from single-star observations to well-known companions to potential exoplanet detections, each exercises the instrument in a different way and each on-sky observation must be justified by new information it brings. We will work with the CPP to develop a rigorous framework for selecting between proposed observations, with well-defined criteria and process for updating targets based on past results. Observing strategies and targets will be optimized to explore instrument properties across a range of parameter space that in turn explores different terms in the contrast budgets and models. Finally, we will work with the data pipeline team on the tools needed to assess performance, including analysis of both images and telemetry. This will also inform observations with scientific objectives such as studies of exoplanets or disks – a well-motivated error framework will provide robust uncertainty and covariance estimates that are critical to modeling science targets, while in turn the comparison of those targets to models (from orbit propagation to planetary atmospheres) helps validate the estimates of uncertainties. Overall, the goal is to help plan and execute observations to ensure CPP teaches us the most for future missions.