Microphysics of Europa’s surface with Galileo/NIMS data

Abstract

Europa’s surface is one of the youngest in the solar system. The Jovian moon is believed to hide a global liquid water ocean under its icy crust [1] and is exposed to intense space weathering due to the continuous bombardment by electrons and ions from Jupiter’s magnetosphere [2]. To understand the processes governing the evolution of the surface it is necessary to finely characterize the microphysics of the ice (composition via endmember volume abundance, grain size and surface roughness). However, the majority of the previous studies [3,4] do not allow to constrain precisely these parameters.   Here we report the use of a radiative transfer model [5] in a Bayesian MCMC inference framework [6,7] to retrieve microphysical properties of Europa's surface using the Galileo Near-Infrared Mapping Spectrometer (NIMS) hyperspectral data [8]. We present the analysis of a calibrated spectrum of a dark lineament from the trailing Anti-jovian hemisphere. The estimated signal-to-noise ratio (SNR) is between 5 and 50, we mainly focus on the 1.0-2.5 µm region for which the SNR is higher with an uncertainty on the absolute calibration up to 10% [8].   A first work has allowed us to test all combinations of 3, 4 and 5 endmembers from a list of 15 relevant compounds [9]. We were able to test over 5,000 combinations and show that some compounds appear necessary to reproduce the observation, such as water ice and sulfuric acid octahydrate, in agreement with previous studies [3,4,10]. However, adding either hydrated sulfates or chlorine salts produces results substantially similar [9]. Here we present a follow-up study in which we focus on the few acceptable combinations identified by our Bayesian inversions and we analyze the results in terms of grain size and surface roughness. We show that the grain size of the mandatory endmembers is well constrained and similar from one combination to another [11]. The macroscopic roughness is however poorly constrained [11], as expected. Thanks to numerical optimizations we are able to invert independently every spectel of a NIMS hyperspectral cube with the bayesian MCMC algorithm. From this result, we present maps of microphysical properties on an entire hyperspectral image of a dark lineament.