Bayesian analysis of Juno/JIRAM's NIR observations of Europa

Abstract

Decades of observations of Europa's surface in the near-infrared (NIR), spanning spacecrafts like Galileo, Cassini and New Horizons, along with ground based observations, have revealed a rich mixture of species on Europa's surface. Adding to the NIR data of Europa, Juno spacecraft's spectrometer JIRAM has observed it in the 2–5 μm wavelength region. Here we present analysis of select spectra from this dataset, focusing on the two forms of water-ice - amorphous and crystalline. We were limited in our ability to include other dominant Europan species, like acid hydrate, due to unavailability of their optical constants over the entire JIRAM wavelength range. We also take this as an opportunity to present a novel Bayesian spectral inversion framework. Traditional spectral fitting methods, for example a grid-based search of parameter space, lack a systematic way to quantify detection significances of the species included in the model, statistically constrain surface properties and explore degenerecies of solution. Our Bayesian inference framework overcomes these shortcomings by confidently detecting amorphous and crystalline ice in the JIRAM data and permits probabilistic constraints on their compositions and average grain sizes to be obtained. We first validate our analysis framework using simulated spectra of amorphous and crystalline ice mixtures and a laboratory spectrum of crystalline ice. We next analyze the JIRAM data and, through Bayesian model comparisons, find that a two-component, intimately mixed model of amorphous and crystalline ice, henceforth referred to as TC-IM, is strongly preferred (at 26σ confidence) over a two-component model of the same materials but where their spectra are areally/linearly mixed. We also find that the TC-IM model is strongly preferred (at > 30σ confidence) over single-component models with only amorphous or crystalline ice, indicating the presence of both these phases of water ice in the data. Given the high SNR of the JIRAM data, abundances and grain sizes of amorphous and crystalline ice are very tightly constrained for the analysis with the TC-IM model. The solution corresponds to a mixture with a very large number density fraction (99.952−0.001+0.001 %) of small (23.12−1.01+1.01 microns) amorphous ice grains, and a very small fraction (0.048−0.001+0.001 %) of large (565.34−1.01+1.01 microns) crystalline ice grains. The overabundance of small amorphous ice grains we find is consistent with previous studies. The maximum-likelihood spectrum of the TC-IM model, however, is in tension with the data in the regions around 2.5 and 3.6 μm, and indicates the presence of non-ice components not currently included in our model. Our new technique therefore holds the promise of being able to identify these minor species hiding in Europan reflectance data in future work and constrain their abundances and physical properties.