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By NASA
To celebrate its third year of revealing stunning scenes of the cosmos in infrared light, NASA’s James Webb Space Telescope has “clawed” back the thick, dusty layers of a section within the Cat’s Paw Nebula (NGC 6334). NASA, ESA, CSA, STScI NASA’s James Webb Space Telescope team released this image of the Cat’s Paw Nebula on July 10, 2025, in honor of the telescope’s third anniversary. Webb’s NIRCam (Near-Infrared Camera) revealed never-before-seen structural details and features: Massive young stars carve away at nearby gas and dust, while their bright starlight produces a bright nebulous glow represented in blue. As a consequence of these massive stars’ lively behavior, the local star formation process will eventually come to a stop.
Take a tour through this section of the Cat’s Paw Nebula.
Image credit: NASA, ESA, CSA, STScI
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By European Space Agency
To mark its third year of highly productive science, astronomers used the NASA/ESA/CSA James Webb Space Telescope to scratch beyond the surface of the Cat’s Paw Nebula (NGC 6334), a massive, local star-forming region.
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By NASA
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NASA’s James Webb Space Telescope’s near-infrared view of the Cat’s Paw Nebula reveals mini “toe beans.” Massive young stars are carving the gas and dust while their bright starlight is producing a bright nebulous glow. Eventually this turbulent region will quench star formation. Full image below. Credits:
NASA, ESA, CSA, STScI. It’s the cat’s meow! To celebrate its third year of revealing stunning scenes of the cosmos in infrared light, NASA’s James Webb Space Telescope has “clawed” back the thick, dusty layers of a section within the Cat’s Paw Nebula (NGC 6334). Focusing Webb’s NIRCam (Near-Infrared Camera) on a single “toe bean” within this active star-forming region revealed a subset of mini toe beans, which appear to contain young stars shaping the surrounding gas and dust.
Webb’s look at this particular area of the Cat’s Paw Nebula just scratches the surface of the telescope’s three years of groundbreaking science.
“Three years into its mission, Webb continues to deliver on its design – revealing previously hidden aspects of the universe, from the star formation process to some of the earliest galaxies,” said Shawn Domagal-Goldman, acting director of the Astrophysics Division at NASA Headquarters in Washington. “As it repeatedly breaks its own records, Webb is also uncovering unknowns for new generations of flagship missions to tackle. Whether it’s following up on the mysteries of dark matter with NASA’s nearly complete Nancy Grace Roman Space Telescope, or narrowing our search for life to Earth-like planets with the Habitable Worlds Observatory, the questions Webb has raised are just as exciting as the answers it’s giving us.”
Image: Cat’s Paw Nebula (NIRCam Image)
NASA’s James Webb Space Telescope’s near-infrared view of the Cat’s Paw Nebula reveals mini “toe beans.” Massive young stars are carving the gas and dust while their bright starlight is producing a bright nebulous glow. Eventually this turbulent region will quench star formation. NASA, ESA, CSA, STScI. Star Formation Flex
The progression from a large molecular cloud to massive stars entails multiple steps, some of which are still not well understood by astronomers. Located approximately 4,000 light-years away in the constellation Scorpius, the Cat’s Paw Nebula offers scientists the opportunity to study the turbulent cloud-to-star process in great detail. Webb’s observation of the nebula in near-infrared light builds upon previous studies by NASA’s Hubble and retired Spitzer Space Telescope in visible- and infrared-light, respectively.
With its sharp resolution, Webb shows never-before-seen structural details and features: Massive young stars are carving away at nearby gas and dust, while their bright starlight is producing a bright nebulous glow represented in blue. It’s a temporary scene where the disruptive young stars, with their relatively short lives and luminosity, have a brief but important role in the region’s larger story. As a consequence of these massive stars’ lively behavior, the local star formation process will eventually come to a stop.
Opera House’s Intricate Structure
Start with the toe bean at top center, which is nicknamed the “Opera House” for its circular, tiered-like structure. The primary drivers for the area’s cloudy blue glow are most likely toward its bottom: either the light from the bright yellowish stars or from a nearby source still hidden behind the dense, dark brown dust.
Just below the orange-brown tiers of dust is a bright yellow star with diffraction spikes. While this massive star has carved away at its immediate surroundings, it has been unable to push the gas and dust away to greater distances, creating a compact shell of surrounding material.
Look closely to notice small patches, like the tuning fork-shaped area to the Opera House’s immediate left, that contain fewer stars. These seemingly vacant zones indicate the presence of dense foreground filaments of dust that are home to still-forming stars and block the light of stars in the background.
Spotlight on Stars
Toward the image’s center are small, fiery red clumps scattered amongst the brown dust. These glowing red sources mark regions where massive star formation is underway, albeit in an obscured manner.
Some massive blue-white stars, like the one in the lower left toe bean, seem to be more sharply resolved than others. This is because any intervening material between the star and the telescope has been dissipated by stellar radiation.
Near the bottom of that toe bean are small, dense filaments of dust. These tiny clumps of dust have managed to remain despite the intense radiation, suggesting that they are dense enough to form protostars. A small section of yellow at the right notes the location of a still-enshrouded massive star that has managed to shine through intervening material.
Across this entire scene are many small yellow stars with diffraction spikes. Bright blue-white stars are in the foreground of this Webb image, but some may be a part of the more expansive Cat’s Paw Nebula area.
One eye-catching aspect of this Webb image is the bright, red-orange oval at top right. Its low count of background stars implies it is a dense area just beginning its star-formation process. A couple of visible and still-veiled stars are scattered throughout this region, which are contributing to the illumination of the material in the middle. Some still-enveloped stars leave hints of their presence, like a bow shock at the bottom left, which indicates an energetic ejection of gas and dust from a bright source.
Further explore this subset of toe beans by embarking on a narrated tour or getting closer to the image. We also invite you to reminisce about Webb’s three years of science observations.
Video A (Narrated Visualization): Cosmic Caverns in the Cat’s Paw Nebula
This visualization explores a subset of toe bean-reminiscent structures within a section of the Cat’s Paw Nebula, a massive, local star-forming region located approximately 4,000 light-years away in the constellation Scorpius. This image by NASA’s James Webb Space Telescope in near-infrared light was released in honor of the telescope’s third science operations anniversary. Since it began science operations in July 2022, Webb’s observations of our universe have wowed scientists and the public alike.
Glide into the lower left toe bean, moving past many small yellow stars along the way, where filaments of gas and dust frame the cavernous area. The region’s nebulous glow, represented in blue, is from the bright light of massive young stars.
Float toward the top toe bean, which is nicknamed the “Opera House” for its circular, tiered-like structure. As you move, you’ll pass plumes of orange-brown dust that vary in density and small, fiery red clumps where star formation is occurring, albeit in an obscured manner.
Credits: Producers: Greg Bacon (STScI), Frank Summers (STScI); Image Processing: Joe DePasquale (STScI); Music: Joe DePasquale (STScI); Designers: Ralf Crawford (STScI), Leah Hustak (STScI), Christian Nieves (STScI), Alyssa Pagan (STScI); Images: NASA, ESA, CSA, STScI; ESO/VISTA.
Video B: Zoom into the Cat’s Paw Nebula
This zoom-in video shows the location of the Cat’s Paw Nebula on the sky. It begins with a ground-based photo by the late astrophotographer Akira Fujii, then shows views from the Digitized Sky Survey. The video then hones in on a select portion of the sky to reveal a European Southern Observatory image of the Cat’s Paw Nebula in visible light. The video continues to zoom in on a section of the Cat’s Paw, which gradually transitions to the stunning image captured by NASA’s James Webb Space Telescope in near-infrared light.
Credits: Video: NASA, ESA, CSA, Danielle Kirshenblat (STScI); Acknowledgement: Akira Fujii, DSS, VISTA. The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).
To learn more about Webb, visit:
https://science.nasa.gov/webb
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Media Contacts
Laura Betz – laura.e.betz@nasa.gov
NASA’s Goddard Space Flight Center, Greenbelt, Md.
Abigail Major – amajor@stsci.edu
Space Telescope Science Institute, Baltimore, Md.
Hannah Braun – hbraun@stsci.edu
Space Telescope Science Institute, Baltimore, Md.
Related Information
View other images of the Cat’s Paw Nebula
Animation Video: “How Dense Pillars Form in Molecular Clouds”
Explore a larger view of the Cat’s Paw Nebula: ViewSpace Video
Read more: Webb Star Formation Discoveries
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Last Updated Jul 09, 2025 Editor Marty McCoy Contact Laura Betz laura.e.betz@nasa.gov Related Terms
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By European Space Agency
The European Space Agency (ESA) successfully established a transmission-reception optical link with NASA’s Deep Space Optical Communications (DSOC) experiment onboard its Psyche mission, located 265 million kilometres away, using two optical grounds stations developed for this purpose in Greece.
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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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