Persistence as an Independent Axis of Atmospheric Interpretability in Exoplanet Target Selection

Atmospheric characterization of transiting exoplanets is commonly prioritized using detectability-based metrics that emphasize expected signal strength, such as the Transmission Spectroscopy Metric (TSM). However, observational experience has shown that large signals do not necessarily correspond to atmospheres that are interpretable in practice, owing to degeneracy, variability, and environmental instability. In this work, I introduce and test atmospheric persistence as an independent axis of optimization for exoplanet target selection. Persistence is operationalized through the Interpretability Per Hour (IPH) metric, which quantifies the expected stability and interpretability of atmospheric constraints per unit observing time. IPH is constructed from physically motivated planetary and stellar properties and is evaluated using confirmed transiting exoplanets drawn from the NASA Exoplanet Archive. I compare IPH directly against TSM across a biologically plausible target subset and show that the two metrics exhibit only weak correlation, indicating that persistence captures information not encoded in detectability alone. A two-axis classification framework (TSM vs. IPH) naturally separates targets into distinct interpretability regimes and recovers known atmospheric successes—including the TRAPPIST-1 planets, LHS 1140 b, and K2-18 b—without tuning. These results demonstrate that atmospheric interpretability constitutes a genuinely independent selection criterion. Incorporating persistence into target prioritization can reduce observational ambiguity, recover high-value targets overlooked by detectability-only approaches, and provide a complementary framework for planning atmospheric characterization programs. This repository contains the manuscript and supporting analysis. The work is intended as a reproducible, testable contribution and may be updated in future versions.