It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Saturday, June 01, 2024
ROBOTICS
Designing environments that are robot-inclusive
SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN
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AN OVERVIEW OF THE DIGITAL TWIN SYSTEM PROPOSED FOR EVALUATING ROBOT-INCLUSIVITY IN THE BUILT ENVIRONMENT
Humans and robots are increasingly interacting within built environments such as cities, buildings, walkways, and parks. Offering adaptability, cost-effectiveness, and scalability, robots are gradually being integrated into various aspects of everyday life, from manufacturing to healthcare to hospitality.
“Ensuring that robots can navigate and operate effectively within built environments is crucial for their widespread adoption and acceptance,” said Associate Professor Mohan Rajesh Elara from the Singapore University of Technology and Design (SUTD).
To have fully autonomous service robots operate in human environments, however, is still a distant goal. Spatial limitations in the built environment restrict a robot’s performance capability. In designing robot-inclusive environments, robot interaction within a built environment must be examined. The current methods used for this involve real-life testing and physical experiments that are costly, time-consuming, and labour-intensive.
To address these limitations, Assoc Prof Mohan and his SUTD team explored an innovative approach in their paper ‘Enhancing robot inclusivity in the built environment: A digital twin-assisted assessment of design guideline compliance’. Here, they demonstrate a novel methodology utilising digital twins to establish the usefulness of built environment design guidelines for robots. They also model some robot archetypes and environments as digital twins to examine robot behaviour within the environments.
A digital twin is a virtual replica of a physical object in a virtual version of its environment. “The digital twin approach offers several key advantages, including the ability to simulate real-world scenarios, enable virtual testing of robot interactions, and provide insights into compliance with design guidelines before physical implementation,” said Assoc Prof Mohan. Moreover, using digital twins allows real-time monitoring, hazard identification, and training a robot’s algorithm before deployment.
In the study, Assoc Prof Mohan uses digital twins to analyse the robot-friendliness of the built environment and prepare for robot deployment. The methodology used is divided into three phases: documentation, digitisation, and design analysis.
First, on-site documentation of the environment is necessary for the simulation. It can be done via direct data collection, laser scanning, or photogrammetry techniques. Ideally performed during the building’s design process, direct data collection uses Building Information Modelling (BIM)—a process of generating and managing digital representations of the building. When a building has already been constructed, laser scanning or photogrammetry techniques can be used to generate point cloud data for processing.
Second, digitisation focuses on making the built environment’s digital model suitable for the robot simulation software. In this step, point cloud data will be reconstructed into a digital space and used to generate three-dimensional (3D) models of the built environment.
Finally, the digital model is designed and analysed. Using the digitised model of the environment in the robot simulation software, the behaviours and interactions of various robots are tested within the environment. Virtual scenarios are made based on existing design guidelines of built environments, and the robots are assessed on their navigation, path planning, and interaction with the surrounding.
In one case study, Assoc Prof Mohan used digital twins to test four different cleaning robots in six different environments that adhered to Accessibility Design Guidelines. Of the four robots, one completed the most goals and performed the best in the simulated environments. It is important to note that robot inclusiveness does not always translate to robot performance efficiency. However, an inclusive environment does promote better accessibility for robots, allowing them to complete their tasks properly.
With robots increasingly being used in urban applications such as cleaning, logistics, and building maintenance, this study’s findings will help improve design guidelines for built environments to accommodate robots. Better design guidelines will allow the seamless integration of robots into human-centric spaces and their enhanced efficiency in various applications.
“The findings could shape future space design by emphasising flexibility, adaptability, and accessibility to accommodate robot interactions,” Assoc Prof Mohan adds.
In the future, the research team aims to extend the current methods and autonomously generate the infrastructure modifications required to improve the accessibility of mobile robots through the use of design, AI and technology. Assoc Prof Mohan also hopes to develop a set of design guidelines and recommendations for building robot-friendly infrastructure.
Enhancing robot inclusivity in the built environment: A digital twin-assisted assessment of design guideline compliance
Navigating new horizons: Pioneering AI
framework enhances robot efficiency
and planning
BEIJING INSTITUTE OF TECHNOLOGY PRESS CO., LTD
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SCIENTISTS AT THE INSTITUTE OF FUTURE TECHNOLOGY AT SHANGHAI UNIVERSITY HAVE USED LARGE LANGUAGE MODELS (LLMS) TO IMPROVE THE EFFICIENCY AND EFFECTIVENESS OF ROBOTS PERFORMING COMPLEX, INSTRUCTION-BASED TASKS.
CREDIT: YUAN ZHANG, SCHOOL OF FUTURE TECHNOLOGY, SHANGHAI UNIVERSITY
In a groundbreaking study published in Cyborg Bionic Systems, researchers from Shanghai University have unveiled a new artificial intelligence framework that revolutionizes the way robots interpret and execute tasks. The "Correction and Planning with Memory Integration" (CPMI) framework leverages large language models (LLMs) to improve the efficiency and effectiveness of robots performing complex, instruction-based tasks.
Traditionally, robots required explicit programming and extensive data to navigate and interact with their environment, often struggling with unexpected challenges or changes in their tasks. However, the team, led by Yuan Zhang and Chao Wang, has introduced a dynamic new approach that integrates memory and planning capabilities within LLMs, enabling robots to adapt and learn from their experiences in real-time.
A Leap Forward in Robotic Task Management
The CPMI framework marks a significant departure from conventional methods by using LLMs not just as tools for processing language but as central decision-making elements in robotic tasks. This innovative use of AI allows robots to break down complex instructions into actionable steps, plan their actions more effectively, and correct their course in response to obstacles or errors.
One of the most striking features of the CPMI framework is its memory module, which gives robots the ability to remember and learn from previous tasks. This capability mimics human memory and experience, enabling robots to perform more efficiently over time and adapt to new situations with unprecedented speed.
Demonstrating Superior Performance
The research team tested their framework using the ALFRED simulation environment, where it outperformed existing models in "few-shot" scenarios—situations where robots have limited examples to learn from. The CPMI framework not only achieved higher success rates but also demonstrated significant improvements in task efficiency and adaptability.
"By integrating memory and planning within a single AI-driven framework, we have enabled robots to learn from each interaction and improve their decision-making processes continuously," explained Chao Wang, the corresponding author of the study. "This not only enhances their performance but also reduces the need for extensive pre-programming and data collection."
Future Applications and Developments
The potential applications for the CPMI framework are vast, ranging from domestic robots that can better assist in household tasks to industrial robots that can navigate complex manufacturing processes. As LLMs continue to evolve, the capabilities of CPMI-equipped robots are expected to grow, leading to more autonomous and intelligent machines.
The Shanghai University team is optimistic about the future of robotic technology and plans to continue refining their framework. "Our next steps involve enhancing the memory capabilities of the CPMI framework and testing it in more diverse and challenging environments," said Yuan Zhang. "We believe that this technology has the potential to transform not just robotics but any field that relies on complex, real-time decision-making."
This research not only sets a new standard for AI in robotics but also opens up new pathways for the integration of advanced AI technologies in everyday life. With the continued development of frameworks like CPMI, the dream of having intelligent, adaptable robots that can perform a wide range of tasks effectively and independently is becoming a tangible reality.
The paper, "Leave It to Large Language Models! Correction and Planning with Memory Integration," was published in the journal Cyborg and Bionic Systems on Mar 27,2024, at DOI: https://spj.science.org/doi/10.34133/cbsystems.0087
Leave It to Large Language Models! Correction and Planning with Memory Integration
R.U.R.
R.U.R. is a 1920 Science fiction play by the Czech writer Karel Čapek. "R.U.R." stands for Rossumovi Univerzální Roboti. The play had its world premiere on 2 January 1921 in Hradec Králové; it introduced the word "robot" to the English language and to science fiction as a whole. R.U.R. became influe...Wikipedia
Mar 22, 2019 ... There will be no poverty. All work will be done by living machines. Everybody will be free from worry and liberated from the degradation of ...
R.U.R.: Directed by Alex Proyas. With Mallory Jansen, Lindsay Farris. A young woman visits the island factory of Rossum's Universal Robots to emancipate the ...
A.I.
Tennessee institutions partner to develop dependable AI for national security applications
DOE/OAK RIDGE NATIONAL LABORATORY
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AT THE TENNESSEE VALLEY CORRIDOR SUMMIT 2024 IN NASHVILLE, TENN., ON WEDNESDAY, VANDERBILT UNIVERSITY AND OAK RIDGE NATIONAL LABORATORY ANNOUNCED A PARTNERSHIP TO DEVELOP TRAINING, TESTING AND EVALUATION METHODS THAT WILL ACCELERATE THE DEPARTMENT OF DEFENSE’S ADOPTION OF AI-BASED SYSTEMS IN OPERATIONAL ENVIRONMENTS.
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 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
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.
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
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SMU ASSOCIATE PROFESSOR OUH ENG LIEH IS DEVELOPING SINGAPORE’S FIRST CHATGPT-BASED PROMPT TUTOR TO PROVIDE REAL-TIME FEEDBACK TO ENHANCE STUDENT LEARNING.
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
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.
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.
NASA’s James Webb Space Telescope finds most distant known galaxy
NASA/GODDARD SPACE FLIGHT CENTER
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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.
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
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.)
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.
CREDIT
ESO
JOURNAL
Science
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Simulations predict intermediate-mass black hole formation in globular clusters
ARTICLE PUBLICATION DATE
30-May-2024
News from "El Gordo": Study suggests dark matter may have collisional properties after all
Using simulations, the research provided a possible explanation for the behaviour observed in this gigantic merging cluster of galaxies
SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI
Contrary to what is established by the standard model, dark matter may indeed be self-interacting. This was the conclusion of a new piece of research published in "Astronomy & Astrophysics" (A&A) and conducted by Riccardo Valdarnini of SISSA's Astrophysics and Cosmology group. Using numerical simulations, the study analysed what happens inside "El Gordo" (literally "The Fat One" in Spanish), a giant cluster merger seven billion light years away from us. The calculations indicated that in this cluster the observed physical separation between the points of maximum density of Dark Matter and those of the other mass components can be explained using the so-called SIDM (Self-Interacting dark matter) model, as opposed to the standard one. This research makes an important contribution in favour of the SIDM model, according to which dark matter particles exchange energy through collisions, with interesting astrophysical repercussions.
"El Gordo": a gigantic cosmic structure for the study of dark matter
"According to the currently-accepted standard cosmological model, the present baryonic matter density of the Universe can account for only 10% of its total matter content. The remaining 90% is in the form of Dark Matter," explains Riccardo Valdarnini, author of the research. "It is generally thought that this matter is non baryonic and made of cold collisionless particles, which respond only to gravity. Hence the name "Cold Dark Matter" (CDM). However, there are still a number of observations which have not yet been explained using the standard model” says the researcher. "To answer these questions, several authors suggest an alternative model, called SIDM." Proving the collisional properties of dark matter and, more generally, alternative theories to the standard cosmological model one is very complicated: "There are, however, unique laboratories that can prove very useful for this purpose, many light years away from us. These are the massive galaxy clusters, gigantic cosmic structures that, upon collision, determine the most energetic events since the Big Bang. With a mass of about 1015 solar masses, El Gordo is one of the largest galaxy clusters we know. Due to its peculiarities, El Gordo has been the subject of numerous studies, both theoretical and observational".
Dark matter could be collisional
According to the standard paradigm, during a cluster merger the behavior of the collisional gas mass component will differ from that of the other two components - galaxies and dark matter. In this scenario, the gas will dissipate part of its initial energy. "This is why, after the collision, the peak of gas mass density will lag behind those of dark matter and galaxies," explains Valdarnini. With the SIDM model, however, a peculiar phenomenon should be observed, namely the physical separation of dark matter centroids - its maximum density points - from those of other mass components with peculiarities that represent a true “Signature of SIDM models”. According to observations, this is exactly what happens inside "El Gordo".
Observing El Gordo
"Let us start with observations:" explains Valdarnini. El Gordo consists of two massive subclusters, respectively denominated northwestern (NW) and southeastern (SE). The X-ray image of the "El Gordo" cluster shows a single X-ray emission peak in the SE subcluster and two faint tails elongated beyond the X-ray peak. A noteworthy feature is the peak location of the different mass components. At variance with what can be seen in the Bullet Cluster, another important example of a colliding cluster, the X-ray peak precedes the SE dark matter peak. Moreover, the Brightest Cluster Galaxy (BCG) is not only trailing the X-ray peak, but it also appears to be spatially offset from the SE mass centroid. Another notable aspect can be seen in the NW cluster, where the galaxy number density peak is spatially offset from the corresponding mass peak."
Research findings suggest Collisional Dark Matter as an explanation for the phenomena observed in "El Gordo"
In order to explain his findings and validate the SIDM models, in the study published in "Astronomy & Astrophysics", Valdarnini used a large set of so-called N-body/hydrodynamical simulations. Thus, he carried out a systematic study aimed at reproducing the observational features of "El Gordo". "The most significant result of this simulation study is that the relative separations observed between the different mass centroids of the "El Gordo" cluster are naturally explained if the dark matter is self-interacting," states Valdarnini. "For this reason, these findings provide an unambiguous signature of a dark matter behaviour that exhibits collisional properties in a very energetic high-redshift cluster collision. There are, however, inconsistencies, as the SIDM cross section values obtained from these simulations are higher than present upper limits, which are of order unity at cluster scales. This suggests that present SIDM models should be considered as only a low order approximation, and that the underlying physical processes that describe the interaction of dark matter in major cluster mergers are more complex than can be adequately represented by the commonly-assumed approach based on the scattering of dark matter particles. The study makes a compelling case for the possibility of self-interacting dark matter between colliding clusters as an alternative to the standard collisionless dark matter paradigm".
CREDIT: SCRIPPS INSTITUTION OF OCEANOGRAPHY AT UC SAN DIEGO
Mars has a distinct structure in its mantle and crust with discernible reservoirs, and this is known thanks to meteorites that scientists at Scripps Institution of Oceanography at UC San Diego and colleagues have analyzed on Earth.
Meteorites that formed roughly 1.3 billion years ago and then ejected from Mars have been collected by scientists from sites in Antarctica and Africa in recent decades. Scripps Oceanography geologist James Day and his colleagues report May 31 in the journal Science Advances on analyses of the chemical compositions of these samples from the Red Planet.
These results are important for understanding not only how Mars formed and evolved, but also for providing precise data that can inform recent NASA missions like Insight and Perseverance and the Mars Sample Return, said study lead Day.
“Martian meteorites are the only physical materials we have available from Mars,” said Day. “They enable us to make precise and accurate measurements and then quantify processes that occurred within Mars and close to the martian surface. They provide direct information on Mars’ composition that can ground truth mission science, like the ongoing Perseverance rover operations taking place there.”
Day’s team assembled its account of Mars’ formation using meteorite samples that all came from the same volcano, known as nakhlites and chassignites. Some 11 million years ago, a large meteor impact on Mars sheared away parts of the planet and sent the rocks hurtling into space. Some of those landed on Earth in the form of meteorites, with the first of these being discovered in 1815 in Chassigny, France and then in 1905 in Nakhla, Egypt.
Since then, more such meteorites have been discovered in locations including Mauritania and Antarctica. Scientists are able to identify Mars as their place of origin because these meteorites are relatively young, so come from a recently active planet, have distinct compositions of the abundant element oxygen compared to Earth, and retain the composition of Mars’ atmosphere measured on the surface by the Viking landers in the 1970s.
The team analyzed the two keystone nakhlite and chassignite meteorite types. Nakhlites are basaltic, similar to lavas erupting in Iceland and Hawaii today, but are rich in a mineral called clinopyroxene. Chassignites are almost exclusively made of the mineral olivine. On Earth, basalts are a main component of the planet’s crust, especially under the oceans, while olivines are abundant in its mantle.
The same is true on Mars. The team showed that these rocks are related to each other through a process known as fractional crystallization within the volcano in which they were formed. Using the composition of these rocks, they also show that some of the then-molten nakhlites incorporated portions of crust close to the surface that also interacted with Mars’ atmosphere.
“By determining that nakhlites and chassignites are from the same volcanic system, and that they interacted with martian crust that was altered by atmospheric interactions, we can identify a new rock type on Mars,” said Day. “With the existing collection of martian meteorites, all of which are volcanic in origin, we are able to better understand the internal structure of Mars.”
The team was able to do this because of the distinctive chemical characteristics of nakhlites and chassignites, as well as the characteristic compositions of other martian meteorites. These reveal an atmospherically altered upper crust to Mars, a complex deeper crust and a mantle where plumes from deep within Mars have penetrated to the base of the crust, while the interior of Mars’, formed early in its evolution have also melted to produce distinct types of volcanoes.
“What’s remarkable is that Mars’ volcanism has incredible similarities, but also differences, to Earth,” said Day. “On the one hand, nakhlites and chassignites formed in similar ways to recent volcanism in places like Oahu in Hawaii. There, newly formed volcanoes press down on the mantle generating tectonic forces that produce further volcanism.”
“On the other hand, the reservoirs in Mars are extremely ancient, separating from one another shortly after the Red planet formed. On Earth, plate tectonics has helped to remix reservoirs back together over time. In this sense, Mars provides an important link between what the early Earth may have looked like from how it looks today.”
Besides Day, Marine Paquet of Scripps Oceanography and colleagues from the University of Nevada Las Vegas and the French National Centre for Scientific Research contributed to the study. The NASA Solar Systems Workings and Emerging Worlds program funded the research.
The Chassigny meteorite in cross-polarized light. This meteorite is dominated by the mineral olivine. Grains are roughly 0.5 millimeters across.
CREDIT
Scripps Institution of Oceanography at UC San Diego
JOURNAL
Science Advances
METHOD OF RESEARCH
Meta-analysis
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
A heterogenous mantle and crustal structure formed during the early differentiation of Mars
ARTICLE PUBLICATION DATE
31-May-2024
Astronomers discover potentially habitable planet
Artist’s impression of the planet (Nasa/JPL-Caltech/R Hurt [Caltech-IPAC])
SAT, 01 JUN, 2024 -
NINA MASSEY, PA SCIENCE CORRESPONDENT
An Earth-like planet with the potential to support human life has been discovered just 40 light-years away.
Named Gliese 12 b, the planet orbits its host star every 12.8 days, and is comparable in size to Venus – so slightly smaller than Earth.
It has an estimated surface temperature of 42C, which is lower than most of the 5,000-odd exoplanets (planets outside of the solar system) confirmed so far.
Astronomers suggest Gliese 12 b is one of the few known planets where humans could theoretically survive, but they are still unsure what its atmosphere looks like, if it has one at all.
Getting an answer to what the atmosphere looks like is vital because it would reveal if the planet can maintain temperatures suitable for liquid water – and possibly life – to exist on its surface.
Masayuki Kuzuhara, a project assistant professor at the Astrobiology Centre in Tokyo, who co-led one research team with Akihiko Fukui, said: “We’ve found the nearest, transiting, temperate, Earth-size world located to date.
“Although we don’t yet know whether it possesses an atmosphere, we’ve been thinking of it as an exo-Venus, with similar size and energy received from its star as our planetary neighbour in the solar system.”
The University of Warwick’s Professor Thomas Wilson, a physicist, was involved in the discovery, using data from Nasa’s satellites to confirm the planet’s existence and characteristics such as its size, temperature, and distance away from Earth.
He said: “This is a really exciting discovery and will help our research into planets similar to Earth.
“Sadly, this planet is a little far away for us to experience it more closely. The light we are seeing now is from 40 years ago – that’s how long it has taken to reach us here on Earth.
Planets like Gliese 12 b are few and far between, so for us to be able to examine one this closely and learn about its atmosphere and temperature is very rare.”
The two teams, including one in Tokyo, used observations by Nasa’s TESS (Transiting Exoplanet Survey Satellite) to help make their discovery.
The planet’s equivalent of the Sun, called Gliese 12, is a cool red dwarf located in constellation Pisces.
The star is only about 27% of the Sun’s size, with about 60% of the Sun’s surface temperature.
Gliese 12 b is not the first Earth-like exoplanet to have been discovered, but Nasa said there are only a handful of worlds like it that warrant a closer look.
It has been billed as a potential target for further investigation by the US space agency’s James Webb Space Telescope.
The newly discovered planet could also be significant because it may help reveal whether the majority of stars in the Milky Way galaxy are capable of hosting temperate planets that have atmospheres and are therefore habitable.
The distance separating the planet and its star is just 7% of the distance between Earth and the Sun, and the planet receives 1.6 times more energy from its star than Earth does from the Sun.
One important factor in retaining an atmosphere is the storminess of its star.
Red dwarfs tend to be magnetically active, resulting in frequent, powerful X-ray flares.
However, analyses by scientists conclude that Gliese 12 shows no signs of extreme behaviour.
“Gliese 12 b represents one of the best targets to study whether Earth-size planets orbiting cool stars can retain their atmospheres, a crucial step to advance our understanding of habitability on planets across our galaxy,” said Shishir Dholakia, a doctoral student at the Centre for Astrophysics at the University of Southern Queensland in Australia.
He co-led a research team with Larissa Palethorpe, a doctoral student at the University of Edinburgh and University College London (UCL).
Co-author Dr Vincent Van Eylen, also from UCL, said: “GJ12b is an incredibly exciting planet because its size is identical to that of Earth.
“Even though GJ12b is about 15 times closer to its star than Earth is to our Sun, because it orbits such a small star the temperature on the planet may be quite similar to that on Earth.
“That doesn’t necessarily guarantee that the planet is habitable, but it does make it a great place to start looking.
“Fortunately it’s also a very nearby star, so we will learn much more about the planet and its atmosphere with telescopes like JWST in the next years.”
