As much as I love experimental and observational data more than I love theory and all the mathematical models that theory builds on, I have to admit that, sometimes, doing a lot of modeling ahead of time makes it possible for experiments to be done much more effectively. Consider the question: How do we know if an icy moon actually has liquid water on the inside? Sometimes we get lucky, and the moons spray water at our spacecraft, but we can’t exactly count on that.

In planning a flyby of Neptune’s moon Triton, researchers realized they’d have about twelve minutes to acquire as much data as they could to determine if the moon does or doesn’t have subsurface water.

In the absence of geysers, researchers have to turn to things like magnetic fields. Large worlds like Neptune have large magnetic fields that can be measurably affected by passage through salty seas in moons and by an atmosphere that also happens to be present. Since both atmosphere and internal oceans can affect magnetic fields, and since neither of them affects the magnetic fields a lot, mission planners realized it was going to be imperative that they figure out exactly what to measure and focus on measuring that.

Enter the modelers. Researchers programmed software to run 13,000 models of a magnetic field interacting with a moon that had variable amounts of atmosphere and ocean and looked to see what measurable features changed and how to isolate collections of things that changed the most. This is called principal component analysis, and it allows researchers to figure out “if we measure these three things, we will know this one thing really well.”

And now, this team has a bunch of software that is ready for that day when someone finally funds a mission to Triton, and that software, with twelve minutes of just the right data, will be able to say ocean world or not.

More Information

Finding Moons’ Hidden Oceans with Induced Magnetic Fields (Eos)

“Single- and Multi-Pass Magnetometric Subsurface Ocean Detection and Characterization in Icy Worlds Using Principal Component Analysis (PCA): Application to Triton,” C. J. Cochrane et al., 2022 January 25, Earth and Space Science