Saturday, June 01, 2024

A.I.

Tennessee institutions partner to develop dependable AI for national security applications

DOE/OAK RIDGE NATIONAL LABORATORY

Artificial intelligence is rapidly becoming one of the most important assets in global competition, including AI-assisted autonomy and decision-making in battlefield applications. However, today’s AI models are vulnerable to novel cyberattacks and could be exploited by adversaries. Moreover, the models are not sufficiently robust and dependable to orchestrate and execute inherently human-centric, mission-critical decisions.

“AI and autonomous vehicles have great potential to let our military operate in contested environments without having to needlessly put our brave men and women in harm’s way — as long as we can trust the AI,” said U.S. Rep. Chuck Fleischmann. “ORNL and Vanderbilt University have the infrastructure and expertise to develop solutions that will give national security leaders the confidence that these AI systems are secure, reliable and dependable.”

Under a new partnership announced during the Tennessee Valley Corridor 2024 National Summit in Nashville this week, Vanderbilt and ORNL will build on complementary research and development capabilities and create science-based AI assurance methods to:

Ensure AI-enabled systems deployed for national security missions are able to function in the most challenging and contested environments.
Test and evaluate the resilience and performance of AI tools at large scales in mission-relevant environments.
Provide decision-makers with the confidence to rapidly adopt and deploy AI-enabled technologies to maintain U.S. competitive advantage.

Vanderbilt’s basic and applied research in the science and engineering of learning-enabled cyber-physical systems, particularly through the renowned Vanderbilt Institute for Software Integrated Systems, provides a foundation for AI assurance research.

“We are excited to partner with Oak Ridge National Laboratory to ensure AI-enabled programs are safe, accurate and reliable at a time when it has never been more imperative to do so,” said Vanderbilt Chancellor Daniel Diermeier. “This radical collaboration among our best researchers and one of the nation’s premier national laboratories will address these crucial challenges head-on. We look forward to the great work we will do together.”

Building on expertise in high-performance computing, data sciences and national security sciences, ORNL recently established the Center for Artificial Intelligence Security Research, or CAISER, to address emerging AI threats. CAISER leads AI security research and AI evaluation at scale, capable of training and testing the largest AI models.

“With ORNL’s unique expertise and capabilities in computing and AI security, we can train, test, analyze and harden AI models using massive datasets,” said ORNL Director Stephen Streiffer. “Working in close cooperation with Vanderbilt, I look forward to advancing the Defense Department’s deployment of AI-based systems for national defense.”

The partnership will initially focus on enabling the U.S. Air Force to fully utilize autonomous vehicles, such as the AI-enabled X-62A VISTA that recently took Air Force Secretary Frank Kendall for a flight featuring simulated threats and combat maneuvers without human intervention. Together, Vanderbilt and ORNL will provide evidence-based assurance that enables Air Force systems to meet DoD’s requirements for Continuous Authorization to Operate in vital national security roles.

“The growth in AI applications is breathtaking, most notably in the commercial marketplace but increasingly in the national defense space as well. While all users of AI are concerned about security and trust of these systems, none is more concerned than the DoD, which is actively developing processes to ensure their appropriate use,” stated Mark Linderman, chief scientist at Air Force Research Laboratory’s Information Directorate. “This partnership will advance the science to enable the U.S. Air Force to confidently field autonomous vehicles, such as the AI-enabled X-62A VISTA, improve situation awareness, and accelerate human decision making.”

Autonomous vehicles operating in a truly independent fashion could be a game-changer for the U.S. military.

“Stewarding our national security and military is one of my greatest responsibilities as a Senator,” said U.S. Sen. Marsha Blackburn. “Tennessee is leading the way in developing the advanced technologies that will ensure our nation’s global leadership and protect the lives of our brave service members.”

The collaborative new research program at Vanderbilt and ORNL continues Tennessee’s tradition of helping the U.S. maintain global leadership.

“Tennessee is once again leading the way to keep Americans safe. This exciting partnership will leverage two world-class institutions and employ their renowned expertise and resources to make our military stronger and more effective,” said U.S. Sen. Bill Hagerty. “Technological dominance is a key pillar of national security, and this partnership will ensure that the Department of Defense can utilize this developing technology in a secure, robust, continuous and dependable fashion.”

About Vanderbilt University

Founded in 1873 as an institution that would “contribute to strengthening the ties that should exist between all sections of our common country,” Vanderbilt University is globally renowned for its transformative education and pathbreaking research. The university’s 10 schools reside on a parklike campus set in the heart of Nashville, Tennessee, contributing to a collaborative culture that empowers leaders of tomorrow and prizes free expression, open inquiry and civil discourse.

Top-ranked in both academics and financial aid, Vanderbilt offers an immersive residential undergraduate experience, with programs in the liberal arts and sciences, engineering, music, education and human development. The university also is home to nationally and internationally recognized graduate schools of law, education, business, medicine, nursing and divinity, and offers robust graduate-degree programs across a range of academic disciplines. Vanderbilt’s prominent alumni base includes Nobel Prize winners, members of Congress, governors, ambassadors, judges, admirals, CEOs, university presidents, physicians, attorneys, and professional sports figures.

Vanderbilt and the affiliated nonprofit Vanderbilt University Medical Center frequently engage in interdisciplinary collaborations to drive positive change across society at large. The two entities recently reached a combined total of more than $1 billion in external research funding in a single year. This landmark achievement reflects the university’s deep commitment to expanding the global impact of its innovation and research as it increases opportunities for faculty, students and staff to pursue bold new ideas and discoveries.

Oak Ridge National Laboratory is managed by UT-Battelle for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.

Tennessee institutions partner to develop dependable AI for national security applications

Moe Khaleel, center, associate laboratory director for national security sciences at ORNL, joined Padma Raghavan, left, vice provost for research and innovation at Vanderbilt University, and Ryan Luley, right, with the Information Warfare Division of the U.S. Air Force Research Laboratory, on Wednesday for a panel discussion titled, "Dependable AI for National Security" during the Tennessee Valley Corridor National Summit 2024 in Nashville, Tenn.

CREDIT

ORNL, U.S. Dept. of Energy

AI-controlled stations can charge electric cars at a personal price

CHALMERS UNIVERSITY OF TECHNOLOGY

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.

Today's commercial charging infrastructure can be a jungle. The market is dynamic and complex with a variety of subscriptions and free competition between providers. At some fast charging stations, congestion and long queues may even occur. In a new study, researchers at Chalmers have created a mathematical model to investigate how fast charging stations controlled by artificial intelligence, AI, can help by offering electric car drivers personalised prices, which the drivers can choose to accept or refuse. The AI uses algorithms that can adjust prices based on individual factors, such as battery level and the car's geographic location.

“The electric car drivers can choose to share information with the charging station providers and receive a personal price proposal from a smart charging station. In our study, we could show how rational and self-serving drivers react by only accepting offers that are beneficial to themselves. This leads to both price and waiting times being minimized”, says Balázs Kulcsár, professor at the department of electrical engineering at Chalmers.

In the study, the drivers always had the option to refuse the personal price, and choose a conventional charging station with a fixed price instead. The personal prices received by the drivers could differ significantly from each other, but were almost always lower than the market prices. For the providers of charging stations, the iterative AI algorithm can find out which individual prices are accepted by the buyer, and under which conditions. However, during the course of the study, the researchers noted that on some occasions the algorithm raised the price significantly when the electric car's batteries were almost completely empty, and the driver consequently had no choice but to accept the offer.

“Smart charging stations can solve complex pricing in a competitive market, but our study shows that they need to be developed and introduced with privacy protection for consumers, well in line with responsible-ethical AI paradigms”, says Balázs Kulcsár.

More about the study

The researchers created a mathematical model of the interaction between profit-maximising fast charging stations and electric car users. The "charging stations" could offer public market prices or AI-driven profit-maximising personal prices, which the "electric car users" could then accept or reject based on their own conditions and needs. In most cases, the results were promising, as the AI-generated prices were lower than the market prices.

The research is presented in the paper: Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach published in the journal Transportation Research, Part C: Emerging Technologies

The researchers involved in the study are Balázs Kulcsár, Sangjun Bae and Sebastian Gros, and they are active at Chalmers University of Technology, Sweden; Seyong Cyber University, China, and Norwegian University of Science and Technology.

The research has been financed by the Swedish Electromobility Center and partially by the EU project E-Laas.

For more information, please contact

Balázs Kulcsár, Professor, Department of Electrical Engineering, Chalmers University of Technology, +46 31-772 17 85, kulcsar@chalmers.se

The contact person speaks English and is available for live and pre-recorded interviews. At Chalmers, we have podcast studios and broadcast filming equipment on site and would be able to assist a request for a television, radio or podcast interview.

JOURNAL

Transportation Research Part C Emerging Technologies

DOI

10.1016/j.trc.2024.104540

METHOD OF RESEARCH

Computational simulation/modeling

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach

Detecting machine-written content in scientific articles

UNIVERSITY OF CHICAGO MEDICAL CENTER

The recent surge in popularity of AI tools such as ChatGPT is forcing the science community to reckon with its place in scientific literature. Prestigious journals such as Science and Nature have attempted to restrict or prohibit AI use in submissions, but are finding it difficult to enforce because of how challenging it is becoming to detect machine-generated language.

Because AI is getting more advanced at mimicking human language, researchers at the University of Chicago were interested in learning how frequently authors are using AI and how well it can produce convincing scientific articles. In a study published in the Journal of Clinical Oncology Clinical Cancer Informatics, Saturday, June 1, Frederick Howard, MD, and colleagues evaluated text from over 15,000 abstracts from the American Society for Clinical Oncology (ASCO) Annual Meeting from 2021 to 2023 using several commercial AI content detectors. They found that there were approximately twice as many abstracts characterized as containing AI content in 2023 as compared to 2021 and 2022 – indicating a clear signal that researchers are utilizing AI tools in scientific writing. Interestingly, the content detectors were much better at distinguishing text generated by older versions of AI chatbots from human-written text, but were less accurate in identifying text from the newer, more accurate AI models or mixtures of human-written and AI-generated text.

As the use of AI in scientific writing will likely increase with the development of more effective AI language models in the coming years, Howard and colleagues warn that it is important that safeguards are instituted to ensure only factually accurate information is included in scientific work given the propensity of AI models to write plausible but incorrect statements. They also concluded that although AI content detectors will never reach perfect accuracy, they could be used as a screening tool to indicate that the presented content requires additional scrutiny from reviewers, but should not be used as the sole means to assess AI content on scientific writing.

JOURNAL

JCO Clinical Cancer Informatics

DOI

10.1200/JCO.2024.42.16_suppl.1565

ARTICLE TITLE

Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts from 2021 to 2023

ARTICLE PUBLICATION DATE

1-Jun-2024

Innovating learning with ChatGPT-based Prompt Tutor

SINGAPORE MANAGEMENT UNIVERSITY

SMU Associate Professor Ouh Eng Lieh — IMAGE:
SMU ASSOCIATE PROFESSOR OUH ENG LIEH IS DEVELOPING SINGAPORE’S FIRST CHATGPT-BASED PROMPT TUTOR TO PROVIDE REAL-TIME FEEDBACK TO ENHANCE STUDENT LEARNING.
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CREDIT: SINGAPORE MANAGEMENT UNIVERSITY

By Jovina Ang

SMU Office of Research – “Giving students immediate and frequent feedback makes online learning more effective,” Associate Professor Ouh Eng Lieh told the Office of Research.

However, based on how most online lessons are designed, questions could not be answered nor doubts clarified until students meet their instructor in the following face-to-face class.

The time delay of a few days to a few weeks can impede student learning as it might make it difficult for students to catch up and understand the subsequent topics in the course.

Learning also does not occur until the knowledge gained is stored in long‐term memory.

The Cognitive Load Theory (CLT) states that for short-term knowledge to be committed to long-term memory, there must be effective cognitive load management, repeated reinforcements and clarification of doubts.

“That’s why providing timely feedback is vital for maintaining a seamless and effective student learning experience. It also helps the student to manage the cognitive load and learn,” Professor Ouh explained to the Office of Research.

He added: “It has been found that reflection prompts, which are essentially reminders to get students to reflect and journal their learnings, can significantly improve learning by getting students to retain and apply what they have learned. Additionally, by leveraging ChatGPT, we can evaluate the degree of understanding by comparing what the student has written to the context or the topic of the lesson.”

“With these in mind, we wanted to develop a learning tool that can provide personalised prompts so that students could either have their questions answered or doubts clarified as they go through their online lesson. This tool, which we have fondly named ‘Prompt Tutor’, will also be programmed to assign additional exercises or generate a quiz for students to hone their learning,” Professor Ouh elaborated.

“And to the best of our knowledge, we believe that this will be the very first Prompt Tutor to be developed in Singapore for teaching computing courses in tertiary institutions,” Professor Ouh went on.

The research

Professor Ouh’s project is funded by an MOE Tertiary Education Research Fund (TRF) grant. An expected three years is needed to develop a fully functioning Prompt Tutor and conduct the experimental research.

He is collaborating with Associate Professor Tan Kar Way and Assistant Professor Lo Siaw Ling, both of whom are from Singapore Management University (SMU), as well as Dr. Lin Feng from Singapore University of Social Sciences (SUSS).

The research builds on Professor Ouh’s previous work where he has successfully developed a Doubt Identification Machine Learning Model to single out doubts from written reflections.

The integration of Prompt Tutor to SMU’s ITSS (Interactive Tutorial Software System) makes it possible for the research team to engineer real time feedback by providing the context to the machine learning algorithms.

The initial part of the research project entails developing a sufficiently accurate Prompt Tutor – essentially a Prompt Tutor that can provide at least 90 percent accurate responses to the student queries or detect doubts in the written reflections.

A total of 80 SMU students enrolled in introductory programming courses will be recruited to participate in the research project. 40 students will be exposed to the Prompt Tutor while the remainder 40 will not be.

There are four steps in the research methodology.

Step 1: Students will be required to watch a five-minute video and write a reflection on what they have learnt.

Step 2: With the use of the Doubt Identification Machine Learning Model, the Prompt Tutor will be programmed to detect doubts or inaccuracies in the student reflections.

Step 3: The Prompt Tutor will then evaluate the students’ written work by checking against the context and accuracy of the topic of the lesson. If any doubt or inaccuracy is detected, the Prompt Tutor will notify the student by sending a personalised prompt such as asking the student a follow-up question for him/her to further reflect and learn.

Step 4: If no inaccuracy is detected, the Prompt Tutor will either prompt the student with more exercises or generate a quiz to test understanding.

Implications of the research

This research has wide ranging applications beyond the teaching of Computer Science or Information Systems courses.

Given that the development of the Prompt Tutor is based on CLT, and therefore, discipline agnostic, its use can be easily extended to the teaching in fields such as Social Science, Medicine, and even languages.

Other than benefiting the students, the Prompt Tutor can benefit instructors – by reducing the time spent on student consultation.

SPACE

Flyby of asteroid Dinkinesh reveals a surprisingly complex history

SwRI-led Lucy mission to Jupiter’s Trojan asteroids finds interesting attractions along the way

Peer-Reviewed Publication

SOUTHWEST RESEARCH INSTITUTE

IMAGE:
AS NASA’S LUCY SPACECRAFT FLEW PAST THE ASTEROID DINKINESH, ITS L’LORRI INSTRUMENT PRODUCED STEREOGRAPHIC IMAGES OF THE NOV. 1, 2023, ENCOUNTER. THE SWRI-LED SCIENCE TEAM ANALYZED PROCESSED IMAGES, IDENTIFYING A TROUGH (YELLOW DOTS) AND RIDGE (ROSE DOTS) ON ITS SURFACE. THE FINAL PANEL SHOWS A SIDE VIEW OF DINKINESH AND ITS SATELLITE SELAM TAKEN A FEW MINUTES AFTER CLOSEST APPROACH.

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CREDIT: NASA/SWRI/JOHNS HOPKINS APL/NOIRLAB

SAN ANTONIO — May 30, 2024 —When NASA’s Lucy spacecraft flew past the tiny main belt asteroid Dinkinesh last November, the Southwest Research Institute-led mission discovered a trough and ridge structure on the main asteroid as well as the first-ever-encountered contact binary satellite. The flyby data of this half-mile-wide object revealed a dramatic history of sudden breakups and transformation.

Scientists think a big chunk of Dinkinesh suddenly shifted, excavating the trough and flinging debris into its vicinity. Some materials fell back to the asteroid body, forming the ridge, while others coalesced to form a contact binary satellite known as Selam. The complex shapes show that Dinkinesh and Selam have significant internal strength and a complex, dynamic history.

“To understand the history of planets like Earth, we need to understand how objects behave when they hit each other, which is affected by the strength of the planetary materials,” said SwRI’s Hal Levison, principal investigator for the Lucy mission and lead author of May 29 paper in Nature discussing this research. “We think the planets formed as zillions of objects orbiting the Sun, like asteroids, ran into each other. Whether objects break apart when they hit or stick together has a lot to do with their strength and internal structure.”

Researchers think that Dinkinesh is revealing its internal structure in how it has responded to stress. Over millions of years, its surface was unevenly heated by the Sun. This slight imbalance caused Dinkinesh to gradually rotate faster. Stress built over time and was suddenly released as a large piece of the asteroid shifted into a more elongated shape.

“The Lucy science team started gathering data about Dinkinesh using telescopes in January 2023, when it was added to our list of targets,” said SwRI’s Simone Marchi, Lucy deputy principal investigator and the paper’s second author. “Thanks to the telescopic data, we thought we had quite a good picture of what Dinkinesh would look like, and we were thrilled to make so many unexpected discoveries.”

If the structure of Dinkinesh were weaker, more like the rubble-pile asteroid Bennu, the fragmented materials would have gradually moved toward the equator and flown off into orbit as it spun faster. However, images suggest Dinkinesh has more cohesive strength, because it could hold together longer, more like a rock that suddenly gives way under stress, fragmenting into large pieces.

“This flyby showed us Dinkinesh has some strength and allowed us to do a little ‘archeology’ to see how this tiny asteroid evolved,” Levison said. “When it broke apart, a disk of material formed, some of which rained back onto the surface, creating the ridge.”

The rest of the disk materials likely formed the double-lobed moon Selam, a contact binary. How this unusual moon ultimately formed remains a mystery, one that the scientists are already digging into.

“We see ridges around asteroids’ equators regularly among near-Earth asteroids, but seeing one up close, around an asteroid with a satellite, helps to unravel some of the possible histories of these binary asteroids,” said SwRI’s Kevin Walsh, an astrophysicist specializing in planetary formation.

Dinkinesh and its satellite are the first two of 11 asteroids that Lucy plans to explore over its 12-year journey. After skimming the inner edge of the main asteroid belt, Lucy is now heading back toward Earth for a gravity assist in December 2024. That close flyby will slingshot the spacecraft back through the main asteroid belt, where it will observe asteroid Donaldjohanson in 2025 en route to the Trojan asteroids, two swarms of ancient bodies that lead and trail Jupiter in its orbit around the Sun. Starting in 2027, Lucy is scheduled to fly past eight Trojans in both asteroid swarms.

Lucy’s principal investigator is from SwRI’s Solar System Science and Exploration Division in Boulder, Colorado. SwRI is based in San Antonio. NASA’s Goddard Space Flight Center in Greenbelt, Maryland, provides overall mission management, systems engineering, and safety and mission assurance. Lockheed Martin Space in Littleton, Colorado, built and operates the spacecraft. Lucy is the 13th mission in NASA’s Discovery Program. NASA’s Marshall Space Flight Center in Huntsville, Alabama, manages the Discovery Program for the Science Mission Directorate at NASA Headquarters in Washington.

For a movie about the Dinkinesh-Selam encounter, visit: https://youtu.be/aE3ixq2yrcw?si=ICxGRZeZFDhYO5Nq

To read the May 29 paper in Nature discussing Dinkinesh research, visit https://www.nature.com/articles/s41586-024-07378-0.

For more information visit https://www.nasa.gov/lucy or https://www.swri.org/planetary-science.

JOURNAL

Nature

NASA’s James Webb Space Telescope finds most distant known galaxy

NASA/GODDARD SPACE FLIGHT CENTER

The JADES-GS-z14-0 Galaxy (shown in the pullout) — IMAGE:
THIS INFRARED IMAGE FROM NASA’S JAMES WEBB SPACE TELESCOPE (ALSO CALLED WEBB OR JWST) WAS TAKEN BY THE NIRCAM (NEAR-INFRARED CAMERA) FOR THE JWST ADVANCED DEEP EXTRAGALACTIC SURVEY, OR JADES, PROGRAM. THE NIRCAM DATA WAS USED TO DETERMINE WHICH GALAXIES TO STUDY FURTHER WITH SPECTROSCOPIC OBSERVATIONS. ONE SUCH GALAXY, JADES-GS-Z14-0 (SHOWN IN THE PULLOUT), WAS DETERMINED TO BE AT A REDSHIFT OF 14.32 (+0.08/-0.20), MAKING IT THE CURRENT RECORD-HOLDER FOR THE MOST DISTANT KNOWN GALAXY. THIS CORRESPONDS TO A TIME LESS THAN 300 MILLION YEARS AFTER THE BIG BANG.
IN THE BACKGROUND IMAGE, BLUE REPRESENTS LIGHT AT 0.9, 1.15, AND 1.5 MICRONS (FILTERS F090W + F115W + F150W), GREEN IS 2.0 AND 2.77 MICRONS (F200W + F277W), AND RED IS 3.56, 4.1, AND 4.44 MICRONS (F356W + F410M + F444W). THE PULLOUT IMAGE SHOWS LIGHT AT 0.9 AND 1.15 MICRONS (F090W + F115W) AS BLUE, 1.5 AND 2.0 MICRONS (F150W + F200W) AS GREEN, AND 2.77 MICRONS (F277W) AS RED.

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CREDIT: CREDIT: NASA, ESA, CSA, STSCI, BRANT ROBERTSON (UC SANTA CRUZ), BEN JOHNSON (CFA), SANDRO TACCHELLA (CAMBRIDGE), PHILL CARGILE (CFA)

Over the last two years, scientists have used NASA’s James Webb Space Telescope (also called Webb or JWST) to explore what astronomers refer to as Cosmic Dawn – the period in the first few hundred million years after the big bang where the first galaxies were born. These galaxies provide vital insight into the ways in which the gas, stars, and black holes were changing when the universe was very young. In October 2023 and January 2024, an international team of astronomers used Webb to observe galaxies as part of the JWST Advanced Deep Extragalactic Survey (JADES) program. Using Webb’s NIRSpec (Near-Infrared Spectrograph), they obtained a spectrum of a record-breaking galaxy observed only two hundred and ninety million years after the big bang. This corresponds to a redshift of about 14, which is a measure of how much a galaxy’s light is stretched by the expansion of the universe. We invited Stefano Carniani from Scuola Normale Superiore in Pisa, Italy, and Kevin Hainline from the University of Arizona in Tucson, Arizona, to tell us more about how this source was found and what its unique properties tell us about galaxy formation.

“The instruments on Webb were designed to find and understand the earliest galaxies, and in the first year of observations as part of the JWST Advanced Deep Extragalactic Survey (JADES), we found many hundreds of candidate galaxies from the first 650 million years after the big bang. In early 2023, we discovered a galaxy in our data that had strong evidence of being above a redshift of 14, which was very exciting, but there were some properties of the source that made us wary. The source was surprisingly bright, which we wouldn’t expect for such a distant galaxy, and it was very close to another galaxy such that the two appeared to be part of one larger object. When we observed the source again in October 2023 as part of the JADES Origins Field, new imaging data obtained with Webb’s narrower NIRCam (Near-Infrared Camera) filters pointed even more toward the high-redshift hypothesis. We knew we needed a spectrum, as whatever we would learn would be of immense scientific importance, either as a new milestone in Webb’s investigation of the early universe or as a confounding oddball of a middle-aged galaxy.

“In January 2024, NIRSpec observed this galaxy, JADES-GS-z14-0, for almost ten hours, and when the spectrum was first processed, there was unambiguous evidence that the galaxy was indeed at a redshift of 14.32, shattering the previous most-distant galaxy record (z = 13.2 of JADES-GS-z13-0). Seeing this spectrum was incredibly exciting for the whole team, given the mystery surrounding the source. This discovery was not just a new distance record for our team; the most important aspect of JADES-GS-z14-0 was that at this distance, we know that this galaxy must be intrinsically very luminous. From the images, the source is found to be over 1,600-light years across, proving that the light we see is coming mostly from young stars and not from emission near a growing supermassive black hole. This much starlight implies that the galaxy is several hundreds of millions of times the mass of the Sun! This raises the question: How can nature make such a bright, massive, and large galaxy in less than 300 million years?

“The data reveal other important aspects of this astonishing galaxy. We see that the color of the galaxy is not as blue as it could be, indicating that some of the light is reddened by dust, even at these very early times. JADES researcher Jake Helton of Steward Observatory and the University of Arizona also identified that JADES-GS-z14-0 was detected at longer wavelengths with Webb’s MIRI (Mid-Infrared Instrument), a remarkable achievement considering its distance. The MIRI observation covers wavelengths of light that were emitted in the visible-light range, which are redshifted out of reach for Webb’s near-infrared instruments. Jake’s analysis indicates that the brightness of the source implied by the MIRI observation is above what would be extrapolated from the measurements by the other Webb instruments, indicating the presence of strong ionized gas emission in the galaxy in the form of bright emission lines from hydrogen and oxygen. The presence of oxygen so early in the life of this galaxy is a surprise and suggests that multiple generations of very massive stars had already lived their lives before we observed the galaxy.

“All of these observations, together, tell us that JADES-GS-z14-0 is not like the types of galaxies that have been predicted by theoretical models and computer simulations to exist in the very early universe. Given the observed brightness of the source, we can forecast how it might grow over cosmic time, and so far we have not found any suitable analogs from the hundreds of other galaxies we’ve observed at high redshift in our survey. Given the relatively small region of the sky that we searched to find JADES-GS-z14-0, its discovery has profound implications for the predicted number of bright galaxies we see in the early universe, as discussed in another concurrent JADES study (Robertson et al., recently accepted). It is likely that astronomers will find many such luminous galaxies, possibly at even earlier times, over the next decade with Webb. We’re thrilled to see the extraordinary diversity of galaxies that existed at Cosmic Dawn!”

These spectroscopic observations were taken as part of Guaranteed Time Observations (GTO) program 1287, and the MIRI ones as part of GTO program 1180.

Medium and mighty: Intermediate-mass black holes can survive in globular clusters

First-ever simulations of individual stars in a forming globular cluster demonstrate potential mechanisms of intermediate-mass black hole formation

SCHOOL OF SCIENCE, THE UNIVERSITY OF TOKYO

Star cluster forming in a giant molecular cloud — IMAGE:
STAR CLUSTER FORMING IN A GIANT MOLECULAR CLOUD REPRODUCED BY THE SIMULATION. THIS IMAGE IS BASED ON THE SIMULATION. BLUE DOTS REPRESENT INDIVIDUAL STARS. DARK AND BRIGHT COLOR INDICATE THE GAS TEMPERATURES (COLD AND HOT). VISUALIZED BY TAKAAKI TAKEDA (VASA ENTERTAINMENT INC.)
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CREDIT: MICHIKO FUJII AND TAKAAKI TAKEDA. 2024

Joint research led by Michiko Fujii of the University of Tokyo demonstrated a possible formation mechanism of intermediate-mass black holes in globular clusters, star clusters that could contain tens of thousands or even millions of tightly packed stars. The first ever star-by-star massive cluster-formation simulations revealed that sufficiently dense molecular clouds, the “birthing nests” of star clusters, can give birth to very massive stars that evolve into intermediate-mass black holes. The findings were published in the journal Science.

“Previous observations have suggested that some massive star clusters (globular clusters) host an intermediate-mass black hole (IMBH),” Fujii explains the motivation for the research project. “An IMBH is a black hole with a mass of 100-10000 solar masses. So far, there has been no strong theoretical evidence to show the existence of IMBH with 1000-10 000 solar masses compared to less massive (stellar mass) and more massive (supermassive) ones.”

Birthing nests might conjure up images of warmth and tranquility. Not so with stars. Globular star clusters form in turmoil. The differences in density first cause stars to collide and merge. As the stars continue to merge and grow, the gravitational forces grow with them. The repeated stellar collisions in the dense, central region of globular clusters are called runaway collisions. They can lead to the birth of very massive stars with more than 1000 solar masses. These stars could potentially evolve into IMBHs. However, previous simulations of already-formed clusters suggested that stellar winds blow away most of their mass, leaving them too small. To investigate whether IMBHs could “survive,” researchers needed to simulate a cluster while it was still forming.

“Star cluster formation simulations were challenging because of the simulation cost,” Fujii says. “We, for the first time, successfully performed numerical simulations of globular cluster formation, modeling individual stars. By resolving individual stars with a realistic mass for each, we could reconstruct the collisions of stars in a tightly packed environment. For these simulations, we have developed a novel simulation code, in which we could integrate millions of stars with high accuracy.”

In the simulation, the runaway collisions indeed led to the formation of very massive stars that evolved into intermediate-mass black holes. The researchers also found that the mass ratio between the cluster and the IMBH matched that of the observations that originally motivated the project.

“Our final goal is to simulate entire galaxies by resolving individual stars,” Fujii points to future research. “It is still difficult to simulate Milky Way-size galaxies by resolving individual stars using currently available supercomputers. However, it would be possible to simulate smaller galaxies such as dwarf galaxies. We also want to target the first clusters, star clusters formed in the early universe. First clusters are also places where IMBHs can be born.”

Omega Centauri, a globular cluster in the Milky-way galaxy. This globular cluster may host an intermediate-mass black hole.