Abstract EANA2025-167

Identifying Regions of Interest (ROIs) on Ocean Worlds for focused study or continuous observations is essential for understanding their complex physical, chemical, biological, and gelogical processes. Furthermore, prioritizing the assessment of habitability conditions—defined by environmental background parameters—and the direct identification of potential biosignatures is crucial to optimize scientific outcomes.
Balancing these scientific priorities with mission constraints is a complex challenge that is compounded by limited resources. Constraints in terms of energy budget, time, and available technologies impact the scope of exploration and restrict the number of ROIs that can be studied. Consequently, sparse data, coupled with the potential low concentration of biosignatures and a high risk of false positives present significant scientific challenges. To address this, a range of complementary approaches from planetary science, geophysics, and data science are proposed.

One promising strategy is multi-modal data integration, that combines independently obtained data from various sources to provide complementary insights into the ice shell or subsurface ocean. Techniques like visible imagery, infrared sensing, and spectroscopy are combined to highlight key features in the ice shell or subsurface ocean. Visible imagery can reveal surface structures, while spectroscopic data may detect chemical anomalies (e.g., salts or organic compounds) that suggest geologically or biologically relevant activity.

For subsurface oceans, key physical and chemical parameters—such as salinity, density, temperature, turbidity, and oxygen concentration—can provide insight into average water properties. Anomalies or deviations from expected baselines may indicate dynamic processes or unique environments worthy of further investigation.

By integrating measurements like the variables mentioned above the system could detect co-occurring anomalies or sharp gradients that suggest biological activity or geochemical transitions. Instead of reacting to single-sensor spikes, a more robust approach may involve evaluating multi-parameter patterns, threshold exceedance, or scoring models to prioritize sampling. This strategy allows for dynamic adaptation to the local environment and could be expanded in future missions with additional sensors. 

When direct measurements are unavailable, model-based inference becomes critical. For example, thermal models can help estimate temperature gradients in the ocean or ice shell, while known current patterns—derived from terrestrial analogs like polar oceans—can inform predictions about circulation and nutrient transport. In this context, physics-based simulations constrained by sparse observational data can generate plausible ROI candidates.

Another effective approach is comparative planetology, where features on one Ocean World are compared with analogous environments on other celestial bodies or Earth. Subglacial lakes in Antarctica represents extreme environments that have been isolated from the atmosphere for millions of years, making them ideal for studying the distribution and activity of unique, isolated life forms. These lakes serve as analogs for  for potential liquid reservoirs beneath icy crusts on Ocean Worlds. The TRIPLE project exemplifies this approach by testing mission transferability to Ocean Worlds through scientific objectives like identifying microorganisms in Antarctic subglacial lakes, studying their biochemical adaptation processes, and investigating the effects of subglacial hydrology on ice sheet dynamics. This research helps interpret similar geological or hydrological features in unfamiliar planetary settings.