In 2015, when NASA’s New Horizons spacecraft encountered the Pluto-Charon system, the Southwest Research Institute-led science team discovered interesting, geologically active objects instead of the inert icy orbs previously envisioned. A SwRI scientist has revisited the data to explore the source of cryovolcanic flows and an obvious belt of fractures on Pluto’s large moon Charon. These new models suggest that when the moon’s internal ocean froze, it may have formed the deep, elongated depressions along its girth but was less likely to lead to cryovolcanoes erupting with ice, water, and other materials in its northern hemisphere.

“A combination of geological interpretations and thermal-orbital evolution models implies that Charon had a subsurface liquid ocean that eventually froze,” said SwRI’s Dr. Alyssa Rhoden, a specialist in the geophysics of icy satellites, particularly those containing oceans, and the evolution of giant planet satellite systems. She authored a new paper on the source of Charon’s surface features in Icarus. “When an internal ocean freezes, it expands, creating large stresses in its icy shell and pressurizing the water below. We suspected this was the source of Charon’s large canyons and cryovolcanic flows.”

New ice forming on the inner layer of the existing ice shell can also stress the surface structure. To better understand the evolution of the moon’s interior and surface, Rhoden modeled how fractures formed in Charon’s ice shell as the ocean beneath it froze. The team modeled oceans of water, ammonia, or a mixture of the two based on questions about the makeup. Ammonia can act as antifreeze and prolong the life of the ocean; however, results did not differ substantially.

When fractures penetrate the entire ice shell and tap the subsurface ocean, the liquid, pressurized by the increase in the volume of the newly frozen ice, can be pushed through the fractures to erupt onto the surface. Models sought to identify the conditions that could create fractures that fully penetrate Charon’s icy shell, linking its surface and subsurface water to allow ocean-sourced cryovolcanism. However, based on current models of Charon’s interior evolution, ice shells were far too thick to be fully cracked by the stresses associated with ocean freezing.

The timing of the ocean freeze is also important. The synchronous and circular orbits of Pluto and Charon stabilized relatively early, so tidal heating only occurred during the first million years.

“Either Charon’s ice shell was less than 6 miles (10 km) thick when the flows occurred, as opposed to the more than 60 miles or 100 km indicated, or the surface was not in direct communication with the ocean as part of the eruptive process,” Rhoden said. “If Charon’s ice shell had been thin enough to be fully cracked, it would imply substantially more ocean freezing than is indicated by the canyons identified on Charon’s encounter hemisphere.”

Fractures in the ice shell may be the initiation points of these canyons along the global tectonic belt of ridges that traverse the face of Charon, separating the northern and southern geological regions of the moon. If additional large extensional features were identified on the hemisphere not imaged by New Horizons, or compositional analysis could prove that Charon’s cryovolcanism originated from the ocean, it would support the idea that its ocean was substantially thicker than expected.

“Ocean freezing also predicts a sequence of geologic activity, in which ocean-sourced cryovolcanism ceases before strain-created tectonism,” Rhoden said. “A more detailed analysis of Charon’s geologic record could help determine whether such a scenario is viable.”

New research from the University of Georgia reveals that artificial intelligence can be used to find planets outside of our solar system. A recent study demonstrated that machine learning can be used to find exoplanets, information that could reshape how scientists detect and identify new planets very far from Earth.

“One of the novel things about this is analyzing environments where planets are still forming,” said Jason Terry, a doctoral student in the UGA Franklin College of Arts and Sciences department of physics and astronomy and lead author on the study. “Machine learning has rarely been applied to the type of data we’re using before, specifically looking at systems that are still actively forming planets.” 

The first exoplanet was found in 1992, and though more than 5,000 are known to exist, those have been among the easiest for scientists to find. Exoplanets at the formation stage are difficult to see for two primary reasons. They are too far away, often hundreds of light-years from Earth, and the discs where they form are thicker than the distance of the Earth to the sun. Data suggests the planets tend to be in the middle of these discs, conveying a signature of dust and gases kicked up by the planet.

The research showed that artificial intelligence can help scientists overcome these difficulties.

“This is a very exciting proof of concept,” said Cassandra Hall, assistant professor of astrophysics, principal investigator of the Exoplanet and Planet Formation Research Group, and co-author of the study. “The power here is that we used exclusively synthetic telescope data generated by computer simulations to train this AI, and then applied it to real telescope data. This has never been done before in our field, and paves the way for a deluge of discoveries as James Webb Telescope data rolls in.”

The James Webb Space Telescope, launched by NASA in 2021, has inaugurated a new level of infrared astronomy, bringing stunning new images and reams of data for scientists to analyze. It’s just the latest iteration of the agency’s quest to find exoplanets, scattered unevenly across the galaxy. The Nancy Grace Roman Observatory, a 2.4-meter survey telescope scheduled to launch in 2027 that will look for dark energy and exoplanets, will be the next major expansion in capability – and delivery of information and data – to comb through the universe for life.

The Webb telescope supplies the ability for scientists to look at exoplanetary systems in an exceptionally bright, high resolution, with the forming environments themselves a subject of great interest as they determine the resulting solar system.

“The potential for good data is exploding, so it’s a very exciting time for the field,” Terry said.

New analytical tools are essential 

Next-generation analytical tools are urgently needed to greet this high-quality data, so scientists can spend more time on theoretical interpretations rather than meticulously combing through the data and trying to find tiny little signatures. 

“In a sense, we’ve sort of just made a better person,” Terry said. “To a large extent the way we analyze this data is you have dozens, hundreds of images for a specific disc and you just look through and ask ‘is that a wiggle?’ then run a dozen simulations to see if that’s a wiggle and …  it’s easy to overlook them – they’re really tiny, and it depends on the cleaning, and so this method is one, really fast, and two, its accuracy gets planets that humans would miss.”

Terry says this is what machine learning can already accomplish – improve the human capacity to save time and money as well as efficiently guide scientific time, investments, and new proposals.

“There remains, within science and particularly astronomy in general, skepticism about machine learning and of AI, a valid criticism of it being this black box – where you have hundreds of millions of parameters and somehow you get out an answer. But we think we’ve demonstrated pretty strongly in this work that machine learning is up to the task. You can argue about interpretation. But in this case, we have very concrete results that demonstrate the power of this method.”

The research team’s work is designed to develop a concrete foundation for future applications on observational data, demonstrating the method’s effectiveness by using simulational observations.