Continuous Habitable Zones: Using Bayesian Methods to Prioritize Characterization of Potentially Habitable Worlds

TL;DR

This paper develops a Bayesian framework to prioritize exoplanets for habitability characterization by estimating the probability of continuous habitability over 2 billion years, aiding resource allocation.

Contribution

It introduces a novel Bayesian method to assess the likelihood of long-term habitability for exoplanets and host stars, improving prioritization strategies.

Findings

Applied to 9 exoplanets and 3 Solar System planets, providing habitability probabilities.

Generated age estimates for 2768 low-mass stars in TESS zones.

Demonstrated the method's utility in ranking planets for follow-up studies.

Abstract

The number of potentially habitable planets continues to increase, but we lack the time and resources to characterize all of them. With $\sim$30 known potentially habitable planets and an ever-growing number of candidate and confirmed planets, a robust statistical framework for prioritizing characterization of these planets is desirable. Using the $\sim$2 Gy it took life on Earth to make a detectable impact on the atmosphere as a benchmark, we use a Bayesian statistical method to determine the probability that a given radius around a star has been continuously habitable for 2 Gy. We perform this analysis on 9 potentially habitable exoplanets with planetary radii $<$1.8 R$_\oplus$ and/or planetary masses $<$10 M$_\oplus$ around 9 low-mass host stars ($\sim$0.5-1.1 M$_\odot$) with measured stellar mass and metallicity, as well as Venus, Earth, and Mars. Ages for the host stars are generated by the analysis. The technique is also used to provide age estimates for 2768 low-mass stars (0.5-1.3 M$_\odot$) in the TESS Continuous Viewing Zones.