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A key goal of exoplanet spectroscopy is to measure atmospheric properties,such as abundances of chemical species, in order to connect them to ourunderstanding of atmospheric physics and planet formation. In this new era ofhigh-quality JWST data, it is paramount that these measurement methods arerobust. When comparing atmospheric models to observations, multiple candidatemodels may produce reasonable fits to the data. Typically, conclusions arereached by selecting the best-performing model according to some metric. Thisignores model uncertainty in favour of specific model assumptions, potentiallyleading to measured atmospheric properties that are overconfident and/orincorrect. In this paper, we compare three ensemble methods for addressingmodel uncertainty by combining posterior distributions from multiple analyses:Bayesian model averaging, a variant of Bayesian model averaging usingleave-one-out predictive densities, and stacking of predictive distributions.We demonstrate these methods by fitting the HST+Spitzer transmission spectrumof the hot Jupiter HD 209458b using models with different cloud and hazeprescriptions. All of our ensemble methods lead to uncertainties on retrievedparameters that are larger, but more realistic, and consistent with physicaland chemical expectations. Since they have not typically accounted for modeluncertainty, uncertainties of retrieved parameters from HST spectra have likelybeen underreported. We recommend stacking as the most robust model combinationmethod. Our methods can be used to combine results from independent retrievalcodes, and from different models within one code. They are also widelyapplicable to other exoplanet analysis processes, such as combining resultsfrom different data reductions.

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis