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“The models were right”: astronomers find ‘missing’ matter
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By NASA
This NASA Hubble Space Telescope image features a dense and dazzling array of blazing stars that form globular cluster ESO 591-12.NASA, ESA, and D. Massari (INAF — Osservatorio di Astrofisica e Scienza dello Spazio); Processing: Gladys Kober (NASA/Catholic University of America) A previously unexplored globular cluster glitters with multicolored stars in this NASA Hubble Space Telescope image. Globular clusters like this one, called ESO 591-12 or Palomar 8, are spherical collections of tens of thousands to millions of stars tightly bound together by gravity. Globular clusters generally form early in the galaxies’ histories in regions rich in gas and dust. Since the stars form from the same cloud of gas as it collapses, they typically hover around the same age. Strewn across this image of ESO 591-12 are a number of red and blue stars. The colors indicate their temperatures; red stars are cooler, while the blue stars are hotter.
Hubble captured the data used to create this image of ESO 591-12 as part of a study intended to resolve individual stars of the entire globular cluster system of the Milky Way. Hubble revolutionized the study of globular clusters since earthbound telescopes are unable to distinguish individual stars in the compact clusters. The study is part of the Hubble Missing Globular Clusters Survey, which targets 34 confirmed Milky Way globular clusters that Hubble has yet to observe.
The program aims to provide complete observations of ages and distances for all of the Milky Way’s globular clusters and investigate fundamental properties of still-unexplored clusters in the galactic bulge or halo. The observations will provide key information on the early stages of our galaxy, when globular clusters formed.
Image credit: NASA, ESA, and D. Massari (INAF — Osservatorio di Astrofisica e Scienza dello Spazio); Processing: Gladys Kober (NASA/Catholic University of America)
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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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Explore Hubble Hubble Home Overview About Hubble The History of Hubble Hubble Timeline Why Have a Telescope in Space? Hubble by the Numbers At the Museum FAQs Science Hubble Science Science Themes Science Highlights Science Behind Discoveries Hubble’s Partners in Science Universe Uncovered Hubble and Artificial Intelligence Explore the Night Sky Impact & Benefits Hubble’s Impact & Benefits Science Impacts Cultural Impact Technology Benefits Impact on Human Spaceflight Astro Community Impacts Observatory Hubble Observatory Hubble Design Mission Operations Missions to Hubble Hubble vs Webb Team Hubble Team Career Aspirations Hubble Astronauts Multimedia Images Videos Sonifications Podcasts e-Books Online Activities 3D Hubble Models Lithographs Fact Sheets Posters Hubble on the NASA App Glossary News Hubble News Social Media Media Resources More 35th Anniversary Online Activities 2 min read
Hubble Observations Give “Missing” Globular Cluster Time to Shine
This NASA Hubble Space Telescope image features a dense and dazzling array of blazing stars that form globular cluster ESO 591-12. NASA, ESA, and D. Massari (INAF — Osservatorio di Astrofisica e Scienza dello Spazio); Processing: Gladys Kober (NASA/Catholic University of America)
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A previously unexplored globular cluster glitters with multicolored stars in this NASA Hubble Space Telescope image. Globular clusters like this one, called ESO 591-12 or Palomar 8, are spherical collections of tens of thousands to millions of stars tightly bound together by gravity. Globular clusters generally form early in the galaxies’ histories in regions rich in gas and dust. Since the stars form from the same cloud of gas as it collapses, they typically hover around the same age. Strewn across this image of ESO 591-12 are a number of red and blue stars. The colors indicate their temperatures; red stars are cooler, while the blue stars are hotter.
Hubble captured the data used to create this image of ESO 591-12 as part of a study intended to resolve individual stars of the entire globular cluster system of the Milky Way. Hubble revolutionized the study of globular clusters since earthbound telescopes are unable to distinguish individual stars in the compact clusters. The study is part of the Hubble Missing Globular Clusters Survey, which targets 34 confirmed Milky Way globular clusters that Hubble has yet to observe.
The program aims to provide complete observations of ages and distances for all of the Milky Way’s globular clusters and investigate fundamental properties of still-unexplored clusters in the galactic bulge or halo. The observations will provide key information on the early stages of our galaxy, when globular clusters formed.
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Claire Andreoli
NASA’s Goddard Space Flight Center, Greenbelt, MD
claire.andreoli@nasa.gov
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Last Updated Jul 03, 2025 Editor Andrea Gianopoulos Location NASA Goddard Space Flight Center Related Terms
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By NASA
A funky effect Einstein predicted, known as gravitational lensing — when a foreground galaxy magnifies more distant galaxies behind it — will soon become common when NASA’s Nancy Grace Roman Space Telescope begins science operations in 2027 and produces vast surveys of the cosmos.
This image shows a simulated observation from NASA’s Nancy Grace Roman Space Telescope with an overlay of its Wide Field Instrument’s field of view. More than 20 gravitational lenses, with examples shown at left and right, are expected to pop out in every one of Roman’s vast observations. A journal paper led by Bryce Wedig, a graduate student at Washington University in St. Louis, Missouri, estimates that of those Roman detects, about 500 from the telescope’s High-Latitude Wide-Area Survey will be suitable for dark matter studies. By examining such a large population of gravitational lenses, the researchers hope to learn a lot more about the mysterious nature of dark matter.Credit: NASA, Bryce Wedig (Washington University), Tansu Daylan (Washington University), Joseph DePasquale (STScI) A particular subset of gravitational lenses, known as strong lenses, is the focus of a new paper published in the Astrophysical Journal led by Bryce Wedig, a graduate student at Washington University in St. Louis. The research team has calculated that over 160,000 gravitational lenses, including hundreds suitable for this study, are expected to pop up in Roman’s vast images. Each Roman image will be 200 times larger than infrared snapshots from NASA’s Hubble Space Telescope, and its upcoming “wealth” of lenses will vastly outpace the hundreds studied by Hubble to date.
Roman will conduct three core surveys, providing expansive views of the universe. This science team’s work is based on a previous version of Roman’s now fully defined High-Latitude Wide-Area Survey. The researchers are working on a follow-up paper that will align with the final survey’s specifications to fully support the research community.
“The current sample size of these objects from other telescopes is fairly small because we’re relying on two galaxies to be lined up nearly perfectly along our line of sight,” Wedig said. “Other telescopes are either limited to a smaller field of view or less precise observations, making gravitational lenses harder to detect.”
Gravitational lenses are made up of at least two cosmic objects. In some cases, a single foreground galaxy has enough mass to act like a lens, magnifying a galaxy that is almost perfectly behind it. Light from the background galaxy curves around the foreground galaxy along more than one path, appearing in observations as warped arcs and crescents. Of the 160,000 lensed galaxies Roman may identify, the team expects to narrow that down to about 500 that are suitable for studying the structure of dark matter at scales smaller than those galaxies.
“Roman will not only significantly increase our sample size — its sharp, high-resolution images will also allow us to discover gravitational lenses that appear smaller on the sky,” said Tansu Daylan, the principal investigator of the science team conducting this research program. Daylan is an assistant professor and a faculty fellow at the McDonnell Center for the Space Sciences at Washington University in St. Louis. “Ultimately, both the alignment and the brightness of the background galaxies need to meet a certain threshold so we can characterize the dark matter within the foreground galaxies.”
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This video shows how a background galaxy’s light is lensed or magnified by a massive foreground galaxy, seen at center, before reaching NASA’s Roman Space Telescope. Light from the background galaxy is distorted, curving around the foreground galaxy and appearing more than once as warped arcs and crescents. Researchers studying these objects, known as gravitational lenses, can better characterize the mass of the foreground galaxy, which offers clues about the particle nature of dark matter.Credit: NASA, Joseph Olmsted (STScI) What Is Dark Matter?
Not all mass in galaxies is made up of objects we can see, like star clusters. A significant fraction of a galaxy’s mass is made up of dark matter, so called because it doesn’t emit, reflect, or absorb light. Dark matter does, however, possess mass, and like anything else with mass, it can cause gravitational lensing.
When the gravity of a foreground galaxy bends the path of a background galaxy’s light, its light is routed onto multiple paths. “This effect produces multiple images of the background galaxy that are magnified and distorted differently,” Daylan said. These “duplicates” are a huge advantage for researchers — they allow multiple measurements of the lensing galaxy’s mass distribution, ensuring that the resulting measurement is far more precise.
Roman’s 300-megapixel camera, known as its Wide Field Instrument, will allow researchers to accurately determine the bending of the background galaxies’ light by as little as 50 milliarcseconds, which is like measuring the diameter of a human hair from the distance of more than two and a half American football fields or soccer pitches.
The amount of gravitational lensing that the background light experiences depends on the intervening mass. Less massive clumps of dark matter cause smaller distortions. As a result, if researchers are able to measure tinier amounts of bending, they can detect and characterize smaller, less massive dark matter structures — the types of structures that gradually merged over time to build up the galaxies we see today.
With Roman, the team will accumulate overwhelming statistics about the size and structures of early galaxies. “Finding gravitational lenses and being able to detect clumps of dark matter in them is a game of tiny odds. With Roman, we can cast a wide net and expect to get lucky often,” Wedig said. “We won’t see dark matter in the images — it’s invisible — but we can measure its effects.”
“Ultimately, the question we’re trying to address is: What particle or particles constitute dark matter?” Daylan added. “While some properties of dark matter are known, we essentially have no idea what makes up dark matter. Roman will help us to distinguish how dark matter is distributed on small scales and, hence, its particle nature.”
Preparations Continue
Before Roman launches, the team will also search for more candidates in observations from ESA’s (the European Space Agency’s) Euclid mission and the upcoming ground-based Vera C. Rubin Observatory in Chile, which will begin its full-scale operations in a few weeks. Once Roman’s infrared images are in hand, the researchers will combine them with complementary visible light images from Euclid, Rubin, and Hubble to maximize what’s known about these galaxies.
“We will push the limits of what we can observe, and use every gravitational lens we detect with Roman to pin down the particle nature of dark matter,” Daylan said.
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 Melbourne, Florida; and Teledyne Scientific & Imaging in Thousand Oaks, California.
By Claire Blome
Space Telescope Science Institute, Baltimore, Md.
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Last Updated Jun 12, 2025 EditorAshley BalzerContactAshley Balzerashley.m.balzer@nasa.govLocationNASA Goddard Space Flight Center Related Terms
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