Jointly Modeling Roman Coronagraph Astrometry and Photometry Improves Orbital Parameter Estimates
TL;DR
An addition to the open-source Python package orbitize!, which allows users to model exoplanet orbits using joint constraints from astrometry and photometric variations due to orbital phase, is discussed, which finds that fitting both astrometry and photometry improves posterior precision relative to fitting astrometry alone.
Abstract
Launching in 2027, the Nancy Roman Grace Space Telescope (Roman) has the potential to directly image exoplanets in reflected light for the first time. Roman imaging will introduce new constraints on exoplanet orbital parameters, since reflected-light intensity depends on orbital phase. In this Note, we discuss an addition to the open-source Python package orbitize!, which allows users to model exoplanet orbits using joint constraints from astrometry and photometric variations due to orbital phase. To investigate the impact of adding photometric data into the orbital model, we simulated realistic measurements of partial orbits, both including and excluding photometry in our model, and computed orbital posteriors. We found that fitting both astrometry and photometry improves posterior precision relative to fitting astrometry alone. This effect was more pronounced for higher-signal noise ratio (SNR) images; for example, photometric data with SNR = 10 yielded 33% improvement in inclination precision when including photometry, while SNR = 3 data yielded only 12% improvement.