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.

• Publication year

  2023

• Venue

  Astronomical Journal

• Publication date

  2023-08-01

• Fields of study

  Physics, Environmental Science

• Identifiers
  DOI 10.3847/1538-3881/acebc3arXiv 2308.00648
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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