Thermal Surface Measurements of Europa using Galileo PPR: Searching for Temperature Anomalies

1. Introduction

Perhaps one of the most fascinating ice-covered moons in our solar system is the Galilean satellite Europa. The successful launch of Europa Clipper has motivated the re-evaluation of our current knowledge of the Jovian moon -- specifically thermal measurements of the moon's surface, which may contain information about recent geologic activity. After the discovery of active plumes on Enceladus [1], similar phenomena were searched for on Europa [2]. While evidence of surface alteration -- such as troughs, ridges, chaos terrain, and the lack of prevalent craters -- indicate ongoing activity and a relatively young surface [3], the presence of plumes is still being debated. 

While no endogenic thermal anomalies have yet been observed on Europa's surface [4], we re-assess the thermal IR data from Galileo Orbiter's photopolarimeter-radiometer instrument (PPR) [5]. We perform a thermal analysis of the surface properties of Europa, including mapping the thermal inertia and albedo similar to what was done by Rathbun et al. [4], with a goal of extending thermal surface mapping beyond the previous 20% surface coverage. We also perform a sensitivity study of PPR in hotspot detection by determining the minimum detectable hotspot temperature across the surface of the moon and compare our results to previous work.

 

2. Data Analysis

We use 29 PPR radiometry datasets taken during various orbits ranging from November 1996 to November 1999. Both narrow band and open filters were used, with a total wavelength range of 0.3-110 μm. We divide the surface into 3°x3° longitude/latitude grid cells and determine each cell's temperature at a given local time to produce diurnal temperature curves. To determine the thermal inertia and albedo, we fit a thermophysical model to each cell's diurnal curve using the Thermophysical Body Model Simulation Script (TEMPEST) [6] as our modelling tool. The best-fit diurnal curve is chosen by minimizing the reduced chi-squared of the model fit, while all data with χ_red² <1 is considered an adequate fit.

We choose three synthetic hotspot areas -- 50, 100, 200 km² -- to represent the possible size range of hotspots based on average sizes of lenticulae [7]. We increase the hotspot temperature by 1 K until the integrated radiance across the PPR filter of the synthetic blackbody exceeds 2σ of the original observation's radiance. The result provides a map of the minimum temperature a hotspot of a given size would need in order to be detected by PPR.

 

3. Results

3.1 Albedo & Thermal Inertia

We calculate the bolometric albedo and thermal inertia for 38% of the surface of Europa (Fig. 1). Our fitting criteria requires at least three data points forming a diurnal curve, with at least one point 45 degrees from noon. These are more relaxed constraints when compared to Rathbun et al. [4], which, alongside the use of more PPR datasets, allows for the increase in surface coverage. This however leads to higher margins of error in our results, which must be taken in account: nearly half of our fits for thermal inertia do not have a constrained upper bound. Nevertheless, these results provide a broad estimate in the possible thermophysical properties of previously unmapped regions. 

We notice lower albedo and thermal inertia in darker regions near the equator, which coincides well with chaos terrain when compared to geological maps [8]. This may provide a physical explanation for variations in albedo and thermal inertia, as opposed to those caused by endogenic emission. Because of this, no thermal anomalies can be verified as of yet based solely on thermal PPR data, which agrees with previous studies [4], [9]. We aim to perform a more detailed comparison between thermophysical properties and geological regions in future work.

Figure 1. Albedo and thermal inertia maps. Base map from Becker et al. [10].

 

3.2 Minimum Hotspot Temperature

Minimum detectable hotspot temperatures for 50 and 200 km² hotspots are displayed in Fig. 2, alongside their respective probability density histograms. The mean for a 200 km² hotspot is 185.89±44.84 K, and 290.86±116.59 K for a 50 km² hotspot. To illustrate agreement in analysis methods, we compare our results to similar work by Rathbun et al. [4] in Fig. 3 for a 100 km² hotspot, plotting only the 15 datasets used in their work. For a 100 km² hotspot, the minimum hotspot temperature detectable by PPR has a mean and standard deviation of 228.98±69.53 K. 

These results provide a visualization of the extent of surface coverage of PPR. Regions in Figs. 2 & 3 with lower hotspot detection thresholds indicate higher resolution nighttime observations, while regions with only daytime or low resolution observations require higher hotspot temperatures to be detected. This highlights areas that would benefit from priority observations from future missions due to their lack of sufficient coverage. 

Figure 2. Minimum detectable hotspot temperature for 50 km² (top) and 200 km² (bottom) hotspots. Histograms for each are plotted to the right.

 

Figure 3. Minimum detectable hotspot temperature for a 100 km² hotspot compared to Rathbun et al. (2010) [4] (lower).

 

References

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