Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
Natasha Latouf, Avi Mandell, Geronimo Villanueva, Michael Moore,, Nicholas Susemiehl, Vincent Kofman, Michael Himes

TL;DR
This study evaluates the optimal observing strategies for detecting water vapor in exoplanet atmospheres using coronagraphic direct imaging, focusing on signal-to-noise ratios, wavelength selection, and molecular abundance.
Contribution
It introduces a grid-based nested sampling approach to efficiently analyze water detectability across multiple bandpasses and atmospheric conditions.
Findings
Water vapor can be detected at 0.74 microns with moderate SNR for high abundances.
Detection at 0.9 microns is feasible with low SNR for modern Earth-like water levels.
Optimal bandpass selection enhances the potential for habitability assessments in exoplanet studies.
Abstract
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 examing 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 (SNR) and molecular abundance across 25 bandpasses in the visible wavelength range (0.5-1 micron). We use a pre-constructed grid consisting of 1.4 million geometric albedo spectra across a range of abundance and pressure, and interpolate the produce forward models for an efficient nested sampling routine, PSGnest. We first test the detectability of H2O in atmospheres that mimix a modern-Earth twin, and then expand…
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Taxonomy
TopicsStellar, planetary, and galactic studies
