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Dr. Nancy A. Levenson Appointed Deputy Director of the Space Telescope Science Institute
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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 NASA
On June 14 and 16, technicians installed solar panels onto NASA’s Nancy Grace Roman Space Telescope, one of the final steps in assembling the observatory. Collectively called the Solar Array Sun Shield, these panels will power and shade the observatory, enabling all the mission’s observations and helping keep the instruments cool.
In this photo, technicians install solar panels onto the outer portion of NASA’s Nancy Grace Roman Space Telescope. Roman’s inner portion is in the background just left of center. By the end of the year, technicians plan to connect the two halves and complete the Roman observatory. Credit: NASA/Sydney Rohde “At this point, the observatory is about 90% complete,” said Jack Marshall, the Solar Array Sun Shield lead at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “We just need to join two large assemblies, and then we’ll run the whole Roman observatory through a series of tests. We’re currently on track for launch several months earlier than the promised date of no later than May 2027.” The team is working toward launch as early as fall 2026.
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Over the course of two days, eight technicians installed Roman's solar panels onto the outer portion of NASA's Nancy Grace Roman Space Telescope. Each of the six panels is about 23 by 33 feet (7 by 10 meters), fitted with photovoltaic cells which will harness energy from sunlight to power the observatory. The solar panels were designed, built, and installed at NASA's Goddard Space Flight Center in Greenbelt, Md.Credit: NASA/Sophia Roberts The Solar Array Sun Shield is made up of six panels, each covered in solar cells. The two central panels will remain fixed to the outer barrel assembly (the observatory’s outer shell) while the other four will deploy once Roman is in space, swinging up to align with the center panels.
The panels will spend the entirety of the mission facing the Sun to provide a steady supply of power to the observatory’s electronics. This orientation will also shade much of the observatory and help keep the instruments cool, which is critical for an infrared observatory. Since infrared light is detectable as heat, excess warmth from the spacecraft’s own components would saturate the detectors and effectively blind the telescope.
The solar panels on NASA’s Nancy Grace Roman Space Telescope are covered in a total of 3,902 solar cells that will convert sunlight directly into electricity much like plants convert sunlight to chemical energy. When tiny bits of light, called photons, strike the cells, some of their energy transfers to electrons within the material. This jolt excites the electrons, which start moving more or jump to higher energy levels. In a solar cell, excited electrons create electricity by breaking free and moving through a circuit, sort of like water flowing through a pipe. The panels are designed to channel that energy to power the observatory.Credit: NASA/Sydney Rohde “Now that the panels have been installed, the outer portion of the Roman observatory is complete,” said Goddard’s Aaron Vigil, a mechanical engineer working on the array. Next, technicians will test deploy the solar panels and the observatory’s “visor” (the deployable aperture cover). The team is also testing the core portion of the observatory, assessing the electronics and conducting a thermal vacuum test to ensure the system operates as planned in the harsh space environment.
This will keep the project on track to connect Roman’s inner and outer segments in November, resulting in a whole observatory by the end of the year that can then undergo pre-launch tests.
Now that the solar panels are installed on the outer portion of NASA’s Nancy Grace Roman Space Telescope, technicians are readying the assembly for vibration testing to ensure it will withstand the extreme shaking experienced during launch.Credit: NASA/Sydney Rohde To virtually tour an interactive version of the telescope, visit: https://roman.gsfc.nasa.gov/interactive/
Download high-resolution video and images from NASA’s Scientific Visualization Studio
The Nancy Grace Roman Space Telescope is managed at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, with participation by NASA’s Jet Propulsion Laboratory in Southern California; Caltech/IPAC in Pasadena, California; the Space Telescope Science Institute in Baltimore; and a science team comprising scientists from various research institutions. The primary industrial partners are BAE Systems Inc. in Boulder, Colorado; L3Harris Technologies in Rochester, New York; and Teledyne Scientific & Imaging in Thousand Oaks, California.
By Ashley Balzer
NASA’s Goddard Space Flight Center, Greenbelt, Md.
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Last Updated Jul 10, 2025 EditorAshley BalzerContactAshley Balzerashley.m.balzer@nasa.govLocationGoddard Space Flight Center Related Terms
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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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