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By NASA
The Goldstone Solar System Radar, part of NASA’s Deep Space Network, made these observations of the recently discovered 500-foot-wide (150-meter-wide) asteroid 2024 MK, which made its closest approach — within about 184,000 miles (295,000 kilometers) of Earth — on June 29.NASA/JPL-Caltech The Deep Space Network’s Goldstone planetary radar had a busy few days observing asteroids 2024 MK and 2011 UL21 as they safely passed Earth.
Scientists at NASA’s Jet Propulsion Laboratory in Southern California recently tracked two asteroids as they flew by our planet. One turned out to have a little moon orbiting it, while the other had been discovered only 13 days before its closest approach to Earth. There was no risk of either near-Earth object impacting our planet, but the radar observations taken during these two close approaches will provide valuable practice for planetary defense, as well as information about their sizes, orbits, rotation, surface details, and clues as to their composition and formation.
Passing Earth on June 27 at a distance of 4.1 million miles (6.6 million kilometers), or about 17 times the distance between the Moon and Earth, the asteroid 2011 UL21 was discovered in 2011 by the NASA-funded Catalina Sky Survey, in Tucson, Arizona. But this is the first time it has come close enough to Earth to be imaged by radar. While the nearly mile-wide (1.5-kilometer-wide) object is classified as being potentially hazardous, calculations of its future orbits show that it won’t pose a threat to our planet for the foreseeable future.
Because close approaches by asteroids the size of 2024 MK are relatively rare, JPL’s planetary radar team gathered as much information about the near-Earth object as possible. This mosaic shows the spinning asteroid in one-minute increments about 16 hours after its closest approach with Earth.NASA/JPL-Caltech Using the Deep Space Network’s 230-foot-wide (70-meter) Goldstone Solar System Radar, called Deep Space Station 14 (DSS-14), near Barstow, California, JPL scientists transmitted radio waves to the asteroid and received the reflected signals by the same antenna. In addition to determining the asteroid is roughly spherical, they discovered that it’s a binary system: A smaller asteroid, or moonlet, orbits it from a distance of about 1.9 miles (3 kilometers).
“It is thought that about two-thirds of asteroids of this size are binary systems, and their discovery is particularly important because we can use measurements of their relative positions to estimate their mutual orbits, masses, and densities, which provide key information about how they may have formed,” said Lance Benner, principal scientist at JPL who helped lead the observations.
These seven radar observations by the Deep Space Network’s Goldstone Solar System Radar shows the mile-wide asteroid 2011 UL21 during its June 27 close approach with Earth from about 4 million miles away. The asteroid and its small moon (a bright dot at the bottom of the image) are circled in white.NASA/JPL-Caltech Second Close Approach
Two days later, on June 29, the same team observed the asteroid 2024 MK pass our planet from a distance of only 184,000 miles (295,000 kilometers), or slightly more than three-quarters of the distance between the Moon and Earth. About 500 feet (150 meters) wide, this asteroid appears to be elongated and angular, with prominent flat and rounded regions. For these observations, the scientists also used DSS-14 to transmit radio waves to the object, but they used Goldstone’s 114-foot (34-meter) DSS-13 antenna to receive the signal that bounced off the asteroid and came back to Earth. The result of this “bistatic” radar observation is a detailed image of the asteroid’s surface, revealing concavities, ridges, and boulders about 30 feet (10 meters) wide.
Close approaches of near-Earth objects the size of 2024 MK are relatively rare, occurring about every couple of decades, on average, so the JPL team sought to gather as much data about the object as possible. “This was an extraordinary opportunity to investigate the physical properties and obtain detailed images of a near-Earth asteroid,” said Benner.
This sunset photo shows NASA’s Deep Space Station 14 (DSS-14), the 230-foot-wide (70-meter) antenna at the Goldstone Deep Space Communications Complex near Barstow, California.NASA/JPL-Caltech The asteroid 2024 MK was first reported on June 16 by the NASA-funded Asteroid Terrestrial-impact Last Alert System (ATLAS) at Sutherland Observing Station in South Africa. Its orbit was changed by Earth’s gravity as it passed by, reducing its 3.3-year orbital period around the Sun by about 24 days. Although it is classified as a potentially hazardous asteroid, calculations of its future motion show that it does not pose a threat to our planet for the foreseeable future.
The Goldstone Solar System Radar Group is supported by NASA’s Near-Earth Object Observations Program within the Planetary Defense Coordination Office at the agency’s headquarters in Washington. Managed by JPL, the Deep Space Network receives programmatic oversight from Space Communications and Navigation program office within the Space Operations Mission Directorate, also at NASA Headquarters.
More information about planetary radar and near-Earth objects can be found at:
https://www.jpl.nasa.gov/asteroid-watch
News Media Contact
Ian J. O’Neill
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-2649
ian.j.oneill@jpl.nasa.gov
2024-097
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Last Updated Jul 03, 2024 Related Terms
Near-Earth Asteroid (NEA) Asteroids Deep Space Network Jet Propulsion Laboratory Planetary Defense Planetary Defense Coordination Office Potentially Hazardous Asteroid (PHA) Space Communications & Navigation Program Explore More
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By NASA
4 Min Read NASA’s Webb Captures Celestial Fireworks Around Forming Star
L1527, shown in this image from NASA’s James Webb Space Telescope’s MIRI (Mid-Infrared Instrument). The colors within this mid-infrared image reveal details about the central protostar’s behavior.
The cosmos seems to come alive with a crackling explosion of pyrotechnics in this new image from NASA’s James Webb Space Telescope. Taken with Webb’s MIRI (Mid-Infrared Instrument), this fiery hourglass marks the scene of a very young object in the process of becoming a star. A central protostar grows in the neck of the hourglass, accumulating material from a thin protoplanetary disk, seen edge-on as a dark line.
The protostar, a relatively young object of about 100,000 years, is still surrounded by its parent molecular cloud, or large region of gas and dust. Webb’s previous observation of L1527, with NIRCam (Near-Infrared Camera), allowed us to peer into this region and revealed this molecular cloud and protostar in opaque, vibrant colors.
Image A: L1527 – Webb/MIRI
L1527, shown in this image from NASA’s James Webb Space Telescope’s MIRI (Mid-Infrared Instrument), is a molecular cloud that harbors a protostar. It resides about 460 light-years from Earth in the constellation Taurus. The more diffuse blue light and the filamentary structures in the image come from organic compounds known as polycyclic aromatic hydrocarbons (PAHs), while the red at the center of this image is an energized, thick layer of gases and dust that surrounds the protostar. The region in between, which shows up in white, is a mixture of PAHs, ionized gas, and other molecules. This image includes filters representing 7.7 microns light as blue, 12.8 microns light as green, and 18 microns light as red.
Both NIRCam and MIRI show the effects of outflows, which are emitted in opposite directions along the protostar’s rotation axis as the object consumes gas and dust from the surrounding cloud. These outflows take the form of bow shocks to the surrounding molecular cloud, which appear as filamentary structures throughout. They are also responsible for carving the bright hourglass structure within the molecular cloud as they energize, or excite, the surrounding matter and cause the regions above and below it to glow. This creates an effect reminiscent of fireworks brightening a cloudy night sky. Unlike NIRCam, however, which mostly shows the light that is reflected off dust, MIRI provides a look into how these outflows affect the region’s thickest dust and gases.
The areas colored here in blue, which encompass most of the hourglass, show mostly carbonaceous molecules known as polycyclic aromatic hydrocarbons. The protostar itself and the dense blanket of dust and a mixture of gases that surround it are represented in red. (The sparkler-like red extensions are an artifact of the telescopes’s optics). In between, MIRI reveals a white region directly above and below the protostar, which doesn’t show as strongly in the NIRCam view. This region is a mixture of hydrocarbons, ionized neon, and thick dust, which shows that the protostar propels this matter quite far away from it as it messily consumes material from its disk.
As the protostar continues to age and release energetic jets, it’ll consume, destroy, and push away much of this molecular cloud, and many of the structures we see here will begin to fade. Eventually, once it finishes gathering mass, this impressive display will end, and the star itself will become more apparent, even to our visible-light telescopes.
The combination of analyses from both the near-infrared and mid-infrared views reveal the overall behavior of this system, including how the central protostar is affecting the surrounding region. Other stars in Taurus, the star-forming region where L1527 resides, are forming just like this, which could lead to other molecular clouds being disrupted and either preventing new stars from forming or catalyzing their development.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).
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).
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Media Contacts
Laura Betz – laura.e.betz@nasa.gov, Rob Gutro – rob.gutro@nasa.gov
NASA’s Goddard Space Flight Center, Greenbelt, Md.
Hanna Braun hbraun@stsci.edu Christine Pulliam – cpulliam@stsci.edu
Space Telescope Science Institute, Baltimore, Md.
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ARTICLE/IMAGE: Webb’s previous observation of L1527, with NIRCam (Near-Infrared Camera)
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Last Updated Jul 02, 2024 Editor Stephen Sabia Contact Laura Betz laura.e.betz@nasa.gov Related Terms
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By NASA
4 min read
NASA-IBM Collaboration Develops INDUS Large Language Models for Advanced Science Research
Named for the southern sky constellation, INDUS (stylized in all caps) is a comprehensive suite of large language models supporting five science domains. NASA By Derek Koehl
Collaborations with private, non-federal partners through Space Act Agreements are a key component in the work done by NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT). A collaboration with International Business Machines (IBM) has produced INDUS, a comprehensive suite of large language models (LLMs) tailored for the domains of Earth science, biological and physical sciences, heliophysics, planetary sciences, and astrophysics and trained using curated scientific corpora drawn from diverse data sources.
INDUS contains two types of models; encoders and sentence transformers. Encoders convert natural language text into numeric coding that can be processed by the LLM. The INDUS encoders were trained on a corpus of 60 billion tokens encompassing astrophysics, planetary science, Earth science, heliophysics, biological, and physical sciences data. Its custom tokenizer developed by the IMPACT-IBM collaborative team improves on generic tokenizers by recognizing scientific terms like biomarkers and phosphorylated. Over half of the 50,000-word vocabulary contained in INDUS is unique to the specific scientific domains used for its training. The INDUS encoder models were used to fine tune the sentence transformer models on approximately 268 million text pairs, including titles/abstracts and questions/answers.
By providing INDUS with domain-specific vocabulary, the IMPACT-IBM team achieved superior performance over open, non-domain specific LLMs on a benchmark for biomedical tasks, a scientific question-answering benchmark, and Earth science entity recognition tests. By designing for diverse linguistic tasks and retrieval augmented generation, INDUS is able to process researcher questions, retrieve relevant documents, and generate answers to the questions. For latency sensitive applications, the team developed smaller, faster versions of both the encoder and sentence transformer models.
Validation tests demonstrate that INDUS excels in retrieving relevant passages from the science corpora in response to a NASA-curated test set of about 400 questions. IBM researcher Bishwaranjan Bhattacharjee commented on the overall approach: “We achieved superior performance by not only having a custom vocabulary but also a large specialized corpus for training the encoder model and a good training strategy. For the smaller, faster versions, we used neural architecture search to obtain a model architecture and knowledge distillation to train it with supervision of the larger model.”
NASA Chief Scientist Kate Calvin gives remarks in a NASA employee town hall on how the agency is using and developing Artificial Intelligence (AI) tools to advance missions and research, Wednesday, May 22, 2024, at the NASA Headquarters Mary W. Jackson Building in Washington. The INDUS suite of models will help facilitate the agency’s AI goals. NASA/Bill Ingalls INDUS was also evaluated using data from NASA’s Biological and Physical Sciences (BPS) Division. Dr. Sylvain Costes, the NASA BPS project manager for Open Science, discussed the benefits of incorporating INDUS: “Integrating INDUS with the Open Science Data Repository (OSDR) Application Programming Interface (API) enabled us to develop and trial a chatbot that offers more intuitive search capabilities for navigating individual datasets. We are currently exploring ways to improve OSDR’s internal curation data system by leveraging INDUS to enhance our curation team’s productivity and reduce the manual effort required daily.”
At the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC), the INDUS model was fine-tuned using labeled data from domain experts to categorize publications specifically citing GES-DISC data into applied research areas. According to NASA principal data scientist Dr. Armin Mehrabian, this fine-tuning “significantly improves the identification and retrieval of publications that reference GES-DISC datasets, which aims to improve the user journey in finding their required datasets.” Furthermore, the INDUS encoder models are integrated into the GES-DISC knowledge graph, supporting a variety of other projects, including the dataset recommendation system and GES-DISC GraphRAG.
Kaylin Bugbee, team lead of NASA’s Science Discovery Engine (SDE), spoke to the benefit INDUS offers to existing applications: “Large language models are rapidly changing the search experience. The Science Discovery Engine, a unified, insightful search interface for all of NASA’s open science data and information, has prototyped integrating INDUS into its search engine. Initial results have shown that INDUS improved the accuracy and relevancy of the returned results.”
INDUS enhances scientific research by providing researchers with improved access to vast amounts of specialized knowledge. INDUS can understand complex scientific concepts and reveal new research directions based on existing data. It also enables researchers to extract relevant information from a wide array of sources, improving efficiency. Aligned with NASA and IBM’s commitment to open and transparent artificial intelligence, the INDUS models are openly available on Hugging Face. For the benefit of the scientific community, the team has released the developed models and will release the benchmark datasets that span named entity recognition for climate change, extractive QA for Earth science, and information retrieval for multiple domains. The INDUS encoder models are adaptable for science domain applications, and the INDUS retriever models support information retrieval in RAG applications.
A paper on INDUS, “INDUS: Effective and Efficient Language Models for Scientific Applications,” is available on arxiv.org.
Learn more about the Science Discovery Engine here.
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Last Updated Jun 24, 2024 Related Terms
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By European Space Agency
Two large asteroids will safely pass Earth this week, a rare occurrence perfectly timed to commemorate this year's Asteroid Day. Neither poses any risk to our planet, but one of them was only discovered a week ago, highlighting the need to continue improving our ability to detect potentially hazardous object in our cosmic neighbourhood.
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By NASA
4 min read
Marshall Research Scientist Enables Large-Scale Open Science
Rahul Ramachandran is a senior research scientist at NASA’s Marshall Space Flight Center. NASA By Jessica Barnett
Most people use tools at work, whether it’s a hammer, a pencil, or a computer. Very few seek a doctorate degree in creating new tools for the job.
Using that degree to make it easier for people around the world to access and use the vast amounts of data gathered by NASA? Well, that might just be unheard of if you didn’t know someone like Rahul Ramachandran, a senior research scientist in the Earth Science branch at NASA’s Marshall Space Flight Center.
“My undergrad was in mechanical engineering. I wanted to do industrial engineering, so I came to the U.S. for that, but I didn’t like the field that much,” Ramachandran explained. “It was by chance somebody suggested meteorology.”
That led him to learn about atmospheric science as well, but it was the 1990s and the technology of the time was very limiting. So, Ramachandran set out to learn more about computers and how to better analyze data.
“The limitations effectively prompted me to get a degree in computer science,” he said. “I now had science, engineering, and computer science in my background. Then, over the years, I got more and more interested in the tools and capabilities that can help not only manage data but also how you extract knowledge from these large datasets.”
Fast forward to today, and Ramachandran is an award-winning scientist helping to ensure the vast amounts of data collected by NASA are accessible and searchable for scientists around the world.
“I never would have thought that I could ever get a job working at an agency like NASA,” he said. “You get to work with some of the smartest people in the world, and you get to work on really hard problems. I think that’s what makes it so intellectually stimulating.”
Over the course of his career, he has worked on many different projects focused on scientific data management, designed frameworks for large scale scientific analysis, and developed machine learning applications. Recently, he worked with team members at IBM Research to create a geospatial AI foundation model that could turn NASA satellite data into maps of natural disasters or other environmental changes. He also established the Interagency Implementation and Advanced Concepts Team (IMPACT) at NASA, which supports NASA’s Earth Science Data Systems Program by collaborating with other agencies and partners to boost the scientific benefits of data collected by NASA’s missions and experiments.
Ramachandran received the 2023 Greg Leptoukh Lecture award for his accomplishments, an honor he attributes in large part to the many collaborators and mentors he’s had over the years.
During his presentation, Ramachandran spoke about the ways in which artificial intelligence can help NASA continue to adapt and support open science.
“We’ve seen what people can do with ChatGPT, which is built on a language foundation model, but there are AI foundation models for science that can be adapted into analyzing scientific data so we can augment what we are doing now in a much more efficient manner,” he said. “It requires a bit of a change in people’s mindset. How do we rethink our processes? How do we rethink a strategy for managing data? How will people search and analyze data information differently? All those things have to be thought of with a different perspective now.”
Such work will have benefits not only for NASA but for those who use the data collected by the agency. Ramachandran said he recently got an email from someone in Africa who was able to use NASA’s data and the geospatial AI foundation model for detecting locust breeding grounds on the continent.
“NASA has produced valuable science data that we make available to the community to use,” Ramachandran said. “I think the future would be that we not only provide the data, but we also provide these AI models that allow the science community to use the data effectively, whether it’s doing basic research or building applications like the locust breeding ground prediction.”
As that future nears, Ramachandran and his team will be ready to help others in the science community find the data they need to learn and build the tools they’ll use for years to come.
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Last Updated Jun 20, 2024 Related Terms
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