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

Abstract: Juno spacecraft's spectrometer JIRAM recently observed the moon Europa in the 2-5 {\mu}m wavelength region. Here we present analysis of the average spectrum of a set of observations near 20{\deg}N and 40{\deg}W, focusing on the two forms of water-ice - amorphous and crystalline. We also take this as an opportunity to present a novel Bayesian spectral inversion framework for reflectance spectroscopy. We first validate this 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 (TC-IM model) of amorphous and crystalline ice is strongly preferred (at 26{\sigma} confidence) over a two-component model of the same species but where their spectra are areally/linearly mixed. We also find that the TC-IM model is strongly preferred (at > 30{\sigma} confidence) over single-component models with only amorphous or crystalline ice, indicating the presence of both these phases of water ice in the data. For the highest SNR estimates of the JIRAM data, the TC-IM model solution corresponds to a mixture with a very large number density fraction (99.952 +/- 0.001 \%) of small (23.12 +/- 1.01 microns) amorphous ice grains, and a very small fraction (0.048 +/- 0.001 \%) of large (565.34 +/- 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 {\mu}m, and indicates the presence of non-ice components not currently included in our model, primarily due to the limited availability of cryogenic optical constants.