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Abstract:

Claims of detections of gases in exoplanet atmospheres often rely on comparisons between models including and excluding specific chemical species. However, the space of molecular combinations available for model construction is vast and highly degenerate. Only a limited subset of these combinations is typically explored for any given detection. As a result, apparent detections of trace gases risk being artefacts of incomplete modelling rather than robust identification of atmospheric constituents, especially in the low-signal-to-noise regime. Here, using the sub-Neptune K2-18 b as a case study, we show that recent biosignature claims vanish when the model space is expanded, with numerous alternatives providing equally good or better fits. We demonstrate that the significance of a claimed detection relies on the choice of models being compared, and that model preference does not in itself imply the presence of a specific gas. We recommend treating model comparisons instead as relative adequacy tests, which should be 91̽��ed by theoretical predictions and complementary metrics of statistical significance to attribute a signal to a particular gas.

Abstract:

ArchNEMESIS is an open-source Python package developed for the analysis of remote sensing spectroscopic observations of planetary atmospheres. It is based on the widely used NEMESIS radiative transfer and retrieval tool, which has been extensively used for the investigation of a wide variety of planetary environments. The main goal of archNEMESIS is to provide the capabilities of its Fortran-based predecessor, keeping or exceeding the efficiency in the calculations, and benefitting from the advantages Python tools provide in terms of usability and portability. ArchNEMESIS enables users to compute synthetic spectra for a wide variety of planetary atmospheres, 91̽��ing multiple spectral ranges, viewing geometries (e.g., nadir, limb, and solar occultation), and radiative transfer scenarios, including multiple scattering. Furthermore, it provides tools to fit observed spectra and retrieve atmospheric and surface parameters using both optimal estimation and nested sampling retrieval schemes. The code, stored in a public GitHub repository under a GPL-v3.0 license, is accompanied by detailed documentation available at https://archnemesis.readthedocs.io/.

Abstract: