Comparative biosignatures with systemic retrievals

TL;DR

This paper proposes a systemic, multi-planet retrieval approach to distinguish biosignature gases from abiotic signals by establishing an abiotic baseline using uninhabited planets within a system, improving attribution confidence.

Contribution

It introduces a novel comparative method leveraging systemic retrievals and Bayesian analysis to empirically define and identify biosignatures against an abiotic baseline in exoplanet systems.

Findings

Systemic retrievals can accurately model abiotic atmospheric conditions.

Biological anomalies are detectable as outliers from the abiotic baseline.

The approach enhances confidence in biosignature attribution using multi-planet data.

Abstract

The discovery of inhabited exoplanets hinges on identifying biosignature gases. JWST can reveal biosignature gases, though current discoveries have yet to evidence life. The central challenge is attribution: how can we confidently identify biogenic sources while ruling out, or deeming unlikely, abiotic explanations? Attribution is particularly difficult for individual planets, especially given the stochastic abiotic processes that can set atmospheric conditions. To address this, we propose a comparative multi-planet approach centred on systemic retrievals: the analysis of multiple planets within a system to empirically define the `abiotic baseline'. This baseline, constructed from obligate uninhabited planets, serves as a local reference point. Systemic retrievals enable marginalisation over inaccessible, latent, shared abiotic parameters within planet evolution models. This is possible because planets within a system are linked by their birth in the same natal disk, have been irradiated by the same evolving star, and have a linked dynamical history. Observations aligning with the abiotic baseline, where the locally-informed abiotic planet evolution models demonstrate high out-of-sample predictive accuracy, are likely non-biological. Potentially biological anomalies are identified as statistical outliers from the abiotic baseline using Bayesian leave-one-out cross-validation. A comparative biosignature is thus defined: an anomaly where a biotic planetary evolution model provides a superior fit than its abiotic counterpart. Where both abiotic and biotic models yield poor predictive accuracy, the anomaly is flagged as an ``unknown unknown"; a signature of either unconstrained abiotic processes, or life as we don't yet know it.