Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE). I. Using Grid-based Nested Sampling in Coronagraphy Observation Simulations for H2O

Detecting H₂O in exoplanet atmospheres is the first step on the path to determining planet habitability. Coronagraphic design currently limits the observing strategy used to detect H₂O, requiring the choice of specific bandpasses to optimize abundance constraints. In order to examine the optimal observing strategy for initial characterization of habitable planets using coronagraph-based direct imaging, we quantify the detectability of H₂O as a function of signal-to-noise ratio (S/N) and molecular abundance across 25 bandpasses in the visible wavelength range (0.5-1 μm). We use a preconstructed grid consisting of 1.4 million geometric albedo spectra across a range of abundance and pressure, and interpolate to produce forward models for an efficient nested sampling routine, PSGnest. We first test the detectability of H₂O in atmospheres that mimic a modern-Earth twin, and then expand to examine a wider range of H₂O abundances; for each abundance value, we constrain the optimal 20% bandpasses based on the effective S/N of the data. We present our findings of H₂O detectability as functions of S/N, wavelength, and abundance, and discuss how to use these results for optimizing future coronographic instrument design. We find that there are specific points in wavelength where H₂O can be detected down to 0.74 μm with moderate-S/N data for abundances at the upper end of Earth's presumed historical values, while at 0.9 μm, detectability is possible with low-S/N data at modern Earth abundances of H₂O.