Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) \RNum{3}: Introducing the KEN
Natasha Latouf, Michael D. Himes, Avi M. Mandell, Michael Dane Moore,, Vincent Kofman, Geronimo L. Villanueva, Chris Stark

TL;DR
This study assesses the detectability of methane in Earth-like exoplanets using spectral data, highlighting the importance of spectral confusion and optimal bandpass selection for future space telescopes.
Contribution
It introduces a new spectral grid analysis method to evaluate methane detectability considering spectral confusion and different bandpass widths for upcoming exoplanet missions.
Findings
Modern-Earth methane is undetectable at typical SNRs.
Archean-Earth methane is detectable across all SNRs and bandpasses.
Methane detectability decreases with higher water vapor abundance.
Abstract
We deploy a newly-generated set of geometric albedo spectral grids to examine the detectability of methane (CH4) in the reflected-light spectrum of an Earth-like exoplanet at visible and near-infrared wavelengths with a future exoplanet imaging mission. By quantifying the detectability as a function of signal-to-noise ratio (SNR) and molecular abundance, we can constrain the best methods of detection with the high-contrast space-based coronagraphy slated for the next generation telescopes such as the Habitable Worlds Observatory (HWO). We used 25 bandpasses between 0.8 and 1.5 microns. The abundances range from a modern-Earth level to an Archean-Earth level, driven by abundances found in available literature. We constrain the optimal 20%, 30%, and 40% bandpasses based on the effective SNR of the data, and investigate the impact of spectral confusion between CH4 and H2O on the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks
