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Can Life Exist on an Icy Moon? NASA’s Europa Clipper Aims to Find Out
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By European Space Agency
Image: This image tells the story of redemption for one lonely star. The young star MP Mus (PDS 66) was thought to be all alone in the Universe, surrounded by nothing but a featureless band of gas and dust called a protoplanetary disc. In most cases, the material inside a protoplanetary disc condenses to form new planets around the star, leaving large gaps where the gas and dust used to be. These features are seen in almost every disc – but not in MP Mus’s.
When astronomers first observed it with the Atacama Large Millimeter/submillimeter Array (ALMA), they saw a smooth, planet-free disc, shown here in the right image. The team, led by Álvaro Ribas, an astronomer at the University of Cambridge, UK, gave this star another chance and re-observed it with ALMA at longer wavelengths that peer even deeper into the protoplanetary disc than before. These new observations, shown in the left image, revealed a gap and a ring that had been obscured in previous observations, suggesting that MP Mus might have company after all.
Meanwhile, another piece of the puzzle was being revealed in Germany as Miguel Vioque, an astronomer at the European Southern Observatory, studied this same star with the European Space Agency’s (ESA’s) Gaia mission. Vioque noticed something suspicious – the star was wobbling. A bit of gravitational detective work, together with insights from the new disc structures revealed by ALMA, showed that this motion could be explained by the presence of a gas giant exoplanet.
Both teams presented their joint results in a new paper published in Nature Astronomy. In what they describe as “a beautiful merging of two groups approaching the same object from different angles”, they show that MP Mus isn’t so boring after all.
[Image description: This is an observation from the ALMA telescope, showing two versions (side-by-side) of a protoplanetary disc. Both discs are bright, glowing yellow-orange objects with a diffused halo against a dark background. The right disc is more smooth and blurry looking. The left disc shows more detail, for example gaps and rings within it.]
Source: ESO
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By NASA
6 min read
Smarter Searching: NASA AI Makes Science Data Easier to Find
Image snapshot taken from NASA Worldview of NASA’s Global Precipitation Measurement (GPM) mission on March 15, 2025 showing heavy rain across the southeastern U.S. with an overlay of the GCMD Keyword Recommender for Earth Science, Atmosphere, Precipitation, Droplet Size. NASA Worldview Imagine shopping for a new pair of running shoes online. If each seller described them differently—one calling them “sneakers,” another “trainers,” and someone else “footwear for exercise”—you’d quickly feel lost in a sea of mismatched terminology. Fortunately, most online stores use standardized categories and filters, so you can click through a simple path: Women’s > Shoes > Running Shoes—and quickly find what you need.
Now, scale that problem to scientific research. Instead of sneakers, think “aerosol optical depth” or “sea surface temperature.” Instead of a handful of retailers, it is thousands of researchers, instruments, and data providers. Without a common language for describing data, finding relevant Earth science datasets would be like trying to locate a needle in a haystack, blindfolded.
That’s why NASA created the Global Change Master Directory (GCMD), a standardized vocabulary that helps scientists tag their datasets in a consistent and searchable way. But as science evolves, so does the challenge of keeping metadata organized and discoverable.
To meet that challenge, NASA’s Office of Data Science and Informatics (ODSI) at the agency’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, developed the GCMD Keyword Recommender (GKR): a smart tool designed to help data providers and curators assign the right keywords, automatically.
Smarter Tagging, Accelerated Discovery
The upgraded GKR model isn’t just a technical improvement; it’s a leap forward in how we organize and access scientific knowledge. By automatically recommending precise, standardized keywords, the model reduces the burden on human curators while ensuring metadata quality remains high. This makes it easier for researchers, students, and the public to find exactly the datasets they need.
It also sets the stage for broader applications. The techniques used in GKR, like applying focal loss to rare-label classification problems and adapting pre-trained transformers to specialized domains, can benefit fields well beyond Earth science.
Metadata Matchmaker
The newly upgraded GKR model tackles a massive challenge in information science known as extreme multi-label classification. That’s a mouthful, but the concept is straightforward: Instead of predicting just one label, the model must choose many, sometimes dozens, from a set of thousands. Each dataset may need to be tagged with multiple, nuanced descriptors pulled from a controlled vocabulary.
Think of it like trying to identify all the animals in a photograph. If there’s just a dog, it’s easy. But if there’s a dog, a bird, a raccoon hiding behind a bush, and a unicorn that only shows up in 0.1% of your training photos, the task becomes far more difficult. That’s what GKR is up against: tagging complex datasets with precision, even when examples of some keywords are scarce.
And the problem is only growing. The new version of GKR now considers more than 3,200 keywords, up from about 430 in its earlier iteration. That’s a sevenfold increase in vocabulary complexity, and a major leap in what the model needs to learn and predict.
To handle this scale, the GKR team didn’t just add more data; they built a more capable model from the ground up. At the heart of the upgrade is INDUS, an advanced language model trained on a staggering 66 billion words drawn from scientific literature across disciplines—Earth science, biological sciences, astronomy, and more.
NASA ODSI’s GCMD Keyword Recommender AI model automatically tags scientific datasets with the help of INDUS, a large language model trained on NASA scientific publications across the disciplines of astrophysics, biological and physical sciences, Earth science, heliophysics, and planetary science. NASA “We’re at the frontier of cutting-edge artificial intelligence and machine learning for science,” said Sajil Awale, a member of the NASA ODSI AI team at MSFC. “This problem domain is interesting, and challenging, because it’s an extreme classification problem where the model needs to differentiate even very similar keywords/tags based on small variations of context. It’s exciting to see how we have leveraged INDUS to build this GKR model because it is designed and trained for scientific domains. There are opportunities to improve INDUS for future uses.”
This means that the new GKR isn’t just guessing based on word similarities; it understands the context in which keywords appear. It’s the difference between a model knowing that “precipitation” might relate to weather versus recognizing when it means a climate variable in satellite data.
And while the older model was trained on only 2,000 metadata records, the new version had access to a much richer dataset of more than 43,000 records from NASA’s Common Metadata Repository. That increased exposure helps the model make more accurate predictions.
The Common Metadata Repository is the backend behind the following data search and discovery services:
Earthdata Search International Data Network Learning to Love Rare Words
One of the biggest hurdles in a task like this is class imbalance. Some keywords appear frequently; others might show up just a handful of times. Traditional machine learning approaches, like cross-entropy loss, which was used initially to train the model, tend to favor the easy, common labels, and neglect the rare ones.
To solve this, NASA’s team turned to focal loss, a strategy that reduces the model’s attention to obvious examples and shifts focus toward the harder, underrepresented cases.
The result? A model that performs better across the board, especially on the keywords that matter most to specialists searching for niche datasets.
From Metadata to Mission
Ultimately, science depends not only on collecting data, but on making that data usable and discoverable. The updated GKR tool is a quiet but critical part of that mission. By bringing powerful AI to the task of metadata tagging, it helps ensure that the flood of Earth observation data pouring in from satellites and instruments around the globe doesn’t get lost in translation.
In a world awash with data, tools like GKR help researchers find the signal in the noise and turn information into insight.
Beyond powering GKR, the INDUS large language model is also enabling innovation across other NASA SMD projects. For example, INDUS supports the Science Discovery Engine by helping automate metadata curation and improving the relevancy ranking of search results.The diverse applications reflect INDUS’s growing role as a foundational AI capability for SMD.
The INDUS large language model is funded by the Office of the Chief Science Data Officer within NASA’s Science Mission Directorate at NASA Headquarters in Washington. The Office of the Chief Science Data Officer advances scientific discovery through innovative applications and partnerships in data science, advanced analytics, and artificial intelligence.
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Last Updated Jul 09, 2025 Related Terms
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By European Space Agency
Astronomers using the European Space Agency’s Cheops mission have caught an exoplanet that seems to be triggering flares of radiation from the star it orbits. These tremendous explosions are blasting away the planet’s wispy atmosphere, causing it to shrink every year.
This is the first-ever evidence for a ‘planet with a death wish’. Though it was theorised to be possible since the nineties, the flares seen in this research are around 100 times more energetic than expected.
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By NASA
Explore This SectionScience Europa Clipper Europa: Ocean World Europa Clipper Home MissionOverview Facts History Timeline ScienceGoals Team SpacecraftMeet Europa Clipper Instruments Assembly Vault Plate Message in a Bottle NewsNews & Features Blog Newsroom Replay the Launch MultimediaFeatured Multimedia Resources About EuropaWhy Europa? Europa Up Close Ingredients for Life Evidence for an Ocean To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
Scientists think there is an ocean within Jupiter’s moon Europa. NASA-JPL astrobiologist Kevin Hand explains why scientists are so excited about the potential of this ice-covered world to answer one of humanity’s most profound questions. Scientists think there is an ocean within Jupiter’s moon Europa. NASA-JPL astrobiologist Kevin Hand explains why scientists are so excited about the potential of this ice-covered world to answer one of humanity’s most profound questions.
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By NASA
Explore This SectionScience Europa Clipper Reddish Bands on Europa Europa Clipper Home MissionOverview Facts History Timeline ScienceGoals Team SpacecraftMeet Europa Clipper Instruments Assembly Vault Plate Message in a Bottle NewsNews & Features Blog Newsroom Replay the Launch MultimediaFeatured Multimedia Resources About EuropaWhy Europa? Europa Up Close Ingredients for Life Evidence for an Ocean This colorized image of Europa is a product of clear-filter grayscale data from one orbit of NASA’s Galileo spacecraft.NASA/JPL-Caltech/SETI Institute Downloads
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May 28, 2025
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This colorized image of Europa is a product of clear-filter grayscale data from one orbit of NASA’s Galileo spacecraft, combined with lower-resolution color data taken on a different orbit.
The blue-white terrains indicate relatively pure water ice, whereas the reddish areas contain water ice mixed with hydrated salts, potentially magnesium sulfate or sulfuric acid. The reddish material is associated with the broad band in the center of the image, as well as some of the narrower bands, ridges, and disrupted chaos-type features. It is possible that these surface features may have communicated with a global subsurface ocean layer during or after their formation.
Part of the terrain in this previously unreleased color view is seen in the monochrome image, PIA01125.
The image area measures approximately 101 by 103 miles (163 km by 167 km). The grayscale images were obtained on November 6, 1997, during the Galileo spacecraft’s 11th orbit of Jupiter, when the spacecraft was approximately 13,237 miles (21,700 kilometers) from Europa. These images were then combined with lower-resolution color data obtained in 1998, during the spacecraft’s 14th orbit of Jupiter, when the spacecraft was 89,000 miles (143,000 km) from Europa.
JPL is a division of the California Institute of Technology in Pasadena.
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