Quantifying the information-use capacity of life detection: A Bayesian framework with Solar System calibration

This paper presents a Bayesian framework for prioritizing life detection using Solar System data as a calibration set. The methodology integrates detection sensitivities, environmental heterogeneity, and prior uncertainties to guide experimental design via expected information gain calculations, defined through mutual information. With only nine Solar System environments (one positive detection), estimates of the life fraction parameter fk,T remain prior-dependent (E[fk,T] = 0.08–0.28). Detection uncertainties widen credible intervals by 40%–60%. The framework explicitly models sensitivity (si,T) and false positive rates (FPRi,T) separately, accounts for time-dependent detection probabilities, and includes robustness checks, such as leave-one-out analyses excluding Earth. The primary contribution is a transparent, reproducible framework for Bayesian updating, not specific numerical predictions. All numerical results presented herein are illustrative and should be interpreted as demonstrations of the framework’s mechanics rather than definitive estimates. Robust inference requires ∼30–50 independent habitat observations, potentially achievable by 2040–2045.