NSF-DOE Vera C. Rubin Observatory, funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science, has achieved a major milestone with the installation of the Legacy Survey of Space and Time (LSST) Camera on the telescope. With the final optical component in place, NSF-DOE Rubin Observatory enters the final phase of testing before capturing the long-awaited and highly anticipated "first look" images, followed by the start of the Legacy Survey of Space and Time. In early March, the NSF-DOE Rubin Observatory team on Cerro Pachón in Chile lifted the car-sized LSST Camera into position on the Simonyi Survey Telescope. This milestone is a significant step forward in the decades-long story of the LSST Camera's design, construction and transport to Chile. Rubin Observatory is jointly funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science. Rubin Observatory is a joint program of NSF NOIRLab and DOE’s SLAC National Accelerator Laboratory, who will cooperatively operate Rubin. "This is the last major step in the construction of one of the most ambitious scientific facilities ever created," said NSF Director Sethuraman Panchanathan. "It's a testament to the technical prowess and dedication of the entire NSF-DOE Rubin Observatory team — and the scientific community that has been striving to get to this point for over two decades." "The installation of the LSST Camera on the telescope is a triumph of science and engineering," said Harriet Kung, Acting Director of the Department of Energy’s Office of Science. "We look forward to seeing the unprecedented images this camera will produce." The LSST Camera was constructed at SLAC, incorporating cutting-edge technology to deliver an unprecedented view of the night sky. "This is a pivotal moment for the teams from all around the world who collaborated to design and build the camera," said Aaron Roodman, director of the LSST Camera and deputy director of Rubin Observatory construction from SLAC. "We will achieve a level of clarity and depth never seen before in images covering the entire Southern Hemisphere sky." After the camera was completed in April 2024, the team transported it to Chile in a carefully coordinated effort to ensure its safe arrival at Rubin Observatory. Credit: NSF-DOE Rubin Observatory/A. Pizarro D. Group photo of the NSF-DOE Vera C. Rubin Observatory team before installing the LSST Camera. "The installation of the LSST Camera is the result of years of meticulous planning and rigorous testing," said Kevin Reil from SLAC, the system integration scientist for Rubin Observatory. "Every step was carefully orchestrated to ensure the camera is positioned with absolute precision. Now, we'll move forward with the final testing phase, bringing us closer than ever to Rubin’s first images." The LSST Camera is the largest digital camera ever built. Weighing over 3000 kilograms, the 3200-megapixel camera is at the center of Rubin Observatory's optical system, which also features an 8.4-meter combined primary/tertiary mirror and a 3.5-meter secondary mirror. Rubin's innovative design enables it to simultaneously capture faint objects and objects that change in position or brightness within its wide field of view. Using the LSST Camera, Rubin Observatory will repeatedly scan the southern night sky for a decade, creating an ultra-wide, ultra-high-definition time-lapse record of the universe. This endeavor will bring the night sky to life, yielding a treasure trove of discoveries, such as asteroids, comets, pulsating stars and supernova explosions. Rubin Observatory data will be used by researchers around the world, enabling groundbreaking scientific discoveries and advancements that will help to understand the universe better, chronicle its evolution, delve into the mysteries of dark energy and dark matter, and reveal answers to questions we have yet to imagine. Installing such a large, delicate piece of equipment was a complex, difficult task. In early March 2025, after months of testing in the clean room on the maintenance level of Rubin Observatory's summit facility, the team on the summit used Rubin's vertical platform lift to move the LSST Camera up to the telescope floor onto a transport cart. Following a carefully planned procedure, the team then used a custom lifting device to carefully position and secure the LSST Camera on the telescope for the first time.  "Mounting the LSST Camera onto the Simonyi Telescope was an effort requiring intense planning, teamwork across the entire observatory and millimeter-precision execution," said Freddy Muñoz, Rubin Observatory mechanical group lead. "Watching the LSST Camera take its place on the telescope is a proud moment for us all." Added Sandra Romero, head of safety for NSF-DOE Rubin Observatory, "Ensuring the safety of our team during this installation was our highest priority. This complex operation was executed with careful planning and adherence to safety protocols, demonstrating the professionalism and commitment of the entire international Rubin team." The LSST Camera utilities and other systems will be connected and tested over the coming weeks. Soon the camera will be ready to start taking detailed images of the night sky, each one so large it would take a wall of 400 ultra-high-definition TV screens to display. This will culminate in a "first look" event when images from the completed Rubin Observatory will be shared with the world for the first time. Travis Lange, LSST Camera project manager from SLAC, said, "It has been a treat to watch the biggest camera the world has ever seen being built by such a talented group of people with such a wide range of backgrounds. It's a wonderful example of what teams of scientists and engineers can accomplish when they are called upon to do what has never been done before." Credit: Hernan Stockebrand/NSF-DOE Rubin Observatory A time-lapse image of the NSF-DOE Vera C. Rubin Observatory on Cerro Pachón in Chile. More information  NSF-DOE Vera C. Rubin Observatory, funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science, is a groundbreaking new astronomy and astrophysics observatory under construction on Cerro Pachón in Chile, with first light expected in mid-2025. It is named after astronomer Vera Rubin, who provided the first convincing evidence for the existence of dark matter. Using the largest camera ever built, Rubin will repeatedly scan the sky for 10 years and create an ultra-wide, ultra-high-definition, time-lapse record of the universe. NSF-DOE Rubin Observatory is a joint initiative of NSF and DOE's Office of Science. Its primary mission is to carry out the Legacy Survey of Space and Time, providing an unprecedented dataset for scientific research supported by both agencies. Rubin is operated jointly by NSF NOIRLab and SLAC National Accelerator Laboratory. NSF NOIRLab is managed by the Association of Universities for Research in Astronomy (AURA), and SLAC is operated by Stanford University for DOE. France provides key support for the construction and operations of Rubin Observatory through contributions from CNRS Nucléaire & Particules. Rubin Observatory is privileged to conduct research in Chile and gratefully acknowledges additional contributions from more than 40 international organizations and teams across 28 countries. NSF is an independent federal agency created by Congress in 1950 to promote the progress of science. NSF supports basic research and people to create knowledge that transforms the future. The DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. NSF NOIRLab is NSF's center for ground-based, optical-infrared astronomy and operates the International Gemini Observatory (a facility of NSF, NRC-Canada, ANID-Chile, MCTIC-Brazil, MINCyT-Argentina and KASI-Republic of Korea), NSF Kitt Peak National Observatory (KPNO), NSF Cerro Tololo Inter-American Observatory (CTIO), the Community Science and Data Center (CSDC) and NSF-DOE Vera C. Rubin Observatory (in cooperation with DOE SLAC National Accelerator Laboratory). It is managed by AURA under a cooperative agreement with NSF and is headquartered in Tucson, Arizona.   The scientific community is honored to have the opportunity to conduct astronomical research on I’oligam Du’ag (Kitt Peak) in Arizona, Maunakea in Hawaii; and Cerro Tololo and Cerro Pachón in Chile. We recognize and acknowledge the very significant cultural role and reverence of I’oligam Du’ag to the Tohono O’odham Nation and Maunakea to the Kanaka Maoli (Native Hawaiian) community. SLAC National Accelerator Laboratory explores how the universe works at the biggest, smallest and fastest scales and invents powerful tools used by researchers around the globe. As world leaders in ultrafast science and bold explorers of the physics of the universe, SLAC forges new ground in understanding our origins and building a healthier and more sustainable future. SLAC's discoveries and innovation help develop new materials and chemical processes and open unprecedented views of the cosmos and life's most delicate machinery. Building on more than 60 years of visionary research, SLAC helps shape the future by advancing areas such as quantum technology, scientific computing and the development of next-generation accelerators. SLAC is operated by Stanford University for DOE's Office of Science.

The computing world is celebrating a major milestone as Andrew Barto, professor emeritus at the University of Massachusetts Amherst, and Richard Sutton, professor of computer science at the University of Alberta, Canada, have been awarded the 2024 Association for Computing Machinery A.M. Turing Award — often called the "Nobel Prize of computing" — for "developing the conceptual and algorithmic foundations of reinforcement learning." The legacy in reinforcement learning Barto and Sutton are widely recognized as pioneers of the modern computational reinforcement learning (RL), a field that addresses the challenge of learning how to act based on evaluative feedback. Their work has laid the conceptual and algorithmic foundations of RL, shaping the future of artificial intelligence and decision-making systems. The influence of RL extends across multiple disciplines, including computer science (machine learning), engineering (optimal control), mathematics (operations research), neuroscience (optimal decision-making), psychology (classical and operant conditioning) and economics (rational choice theory). Researchers in these fields continue to be profoundly shaped by the contributions of Sutton and Barto. From NSF Grants to AI Breakthroughs Barto's contributions were made possible through a series of U.S. National Science Foundation-funded projects that sustained AI research long before its recent boom. His research was supported through grants from NSF programs including the National Robotics Initiative, Robust Intelligence, Collaborative Research in Computation Neuroscience, Human-Centered Computing, Biological Information Technology and Systems, Artificial Intelligence and Cognitive Science, which have driven the long-term, fundamental advances in machine learning that we see today. "Barto's research exemplifies the power of foundational computational research that has not only advanced state-of-the-art decision-making machines and intelligent systems but has also provided critical insights into understanding intelligence itself," said Greg Hager, NSF assistant director for Computer and Information Science and Engineering. "Andy Barto's work laid the foundation for modern reinforcement learning, influencing generations of researchers, including myself. His insights with Rich Sutton into how agents can learn and adapt in complex environments form the backbone of how automated behavior is generated in the field of artificial intelligence. Without his pioneering research, many of today's — and tomorrow's — AI breakthroughs wouldn't be possible," said Michael Littman, director for the NSF Division of Information and Intelligent Systems. The impact of Barto and Sutton's work For decades, NSF has supported fundamental research in AI, with Barto's work being among the most influential. Barto and Sutton formalized RL concepts through decades of research, beginning with Sutton's time as Barto's first doctoral student. Their collaboration continued as Sutton later joined Barto at the UMass Amherst as a senior research scientist from 1995 to 1998 and beyond, producing many of the foundational RL approaches that remain in use today. Reinforcement learning methods built on Sutton and Barto's work today underpin: Chatbots: Conversational AI agents learn to answer questions helpfully and accurately with the help of a technique called reinforcement learning from human feedback, as deployed in ChatGPT and other leading bots. Games: From Jeopardy to Go to video games, RL algorithms have made it possible for computer players to achieve world-class performance and have even influenced the strategies of the best human players. Robot motor skill learning: RL enables robots to learn autonomously through trial and error how to carry out intricate tasks. Microprocessor layout and circuit design: RL systems make decisions for composing components that make up computer chips Personalized recommendations: Online services like Netflix and YouTube rely on RL techniques to tailor recommendations. Autonomous vehicles: RL models help self-driving cars learn how to navigate complex traffic environments. Supply chain optimization: RL-enabled systems learn what items need to be stored where so that customers can receive goods quickly and cheaply. Algorithm design: Researchers have broken new ground and solved long-standing problems with the help of RL systems. Breakthroughs in RL have fueled a multibillion-dollar industry, with major companies like DeepMind and OpenAI relying on RL as a core technology. Additionally, many major tech firms now have dedicated RL research teams. It is also recognized as a core topic of study. For example, RL was added to the Computer Science Standards of Learning for Virginia Public Schools earlier this year. Bridging AI and neuroscience The influence of Barto and Sutton's work extends far beyond computer science and AI, forging crucial connections between RL and brain sciences, including cognitive science, psychology and neuroscience. Their research has provided groundbreaking insights into how learning can occur, both in machines and in the human brain. One of their earliest breakthroughs came in 1981 when they showed that temporal difference (TD) learning could explain certain learning behaviors that the existing Rescorla-Wagner model couldn't. This discovery opened the door to a new way of understanding how learning happens. Building on this idea, a 1995 study found a connection between the TD algorithm and how dopamine neurons in the brain behave. This insight laid the groundwork for later experiments that confirmed that TD learning accurately describes how dopamine influences reward-based learning. With the 2025 A.M. Turing Award recognizing Barto and Sutton's lifetime achievements, their legacy underscores the importance of sustained federal investment in basic research — the kind of support that has fueled AI's breakthroughs over the last four decades. For more details on this year's award, please visit https://amturing.acm.org/

The computing world is celebrating a major milestone as Andrew Barto, professor emeritus at the University of Massachusetts Amherst, and Richard Sutton, professor of computer science at the University of Alberta, Canada, have been awarded the 2024 Association for Computing Machinery A.M. Turing Award — often called the "Nobel Prize of computing" — for "developing the conceptual and algorithmic foundations of reinforcement learning." The legacy in reinforcement learning Barto and Sutton are widely recognized as pioneers of the modern computational reinforcement learning (RL), a field that addresses the challenge of learning how to act based on evaluative feedback. Their work has laid the conceptual and algorithmic foundations of RL, shaping the future of artificial intelligence and decision-making systems. The influence of RL extends across multiple disciplines, including computer science (machine learning), engineering (optimal control), mathematics (operations research), neuroscience (optimal decision-making), psychology (classical and operant conditioning) and economics (rational choice theory). Researchers in these fields continue to be profoundly shaped by the contributions of Sutton and Barto. From NSF Grants to AI Breakthroughs Barto's contributions were made possible through a series of U.S. National Science Foundation-funded projects that sustained AI research long before its recent boom. His research was supported through grants from NSF programs including the National Robotics Initiative, Robust Intelligence, Collaborative Research in Computation Neuroscience, Human-Centered Computing, Biological Information Technology and Systems, Artificial Intelligence and Cognitive Science, which have driven the long-term, fundamental advances in machine learning that we see today. "Barto's research exemplifies the power of foundational computational research that has not only advanced state-of-the-art decision-making machines and intelligent systems but has also provided critical insights into understanding intelligence itself," said Greg Hager, NSF assistant director for Computer and Information Science and Engineering. "Andy Barto's work laid the foundation for modern reinforcement learning, influencing generations of researchers, including myself. His insights with Rich Sutton into how agents can learn and adapt in complex environments form the backbone of how automated behavior is generated in the field of artificial intelligence. Without his pioneering research, many of today's — and tomorrow's — AI breakthroughs wouldn't be possible," said Michael Littman, director for the NSF Division of Information and Intelligent Systems. The impact of Barto and Sutton's work For decades, NSF has supported fundamental research in AI, with Barto's work being among the most influential. Barto and Sutton formalized RL concepts through decades of research, beginning with Sutton's time as Barto's first doctoral student. Their collaboration continued as Sutton later joined Barto at the UMass Amherst as a senior research scientist from 1995 to 1998 and beyond, producing many of the foundational RL approaches that remain in use today. Reinforcement learning methods built on Sutton and Barto's work today underpin: Chatbots: Conversational AI agents learn to answer questions helpfully and accurately with the help of a technique called reinforcement learning from human feedback, as deployed in ChatGPT and other leading bots. Games: From Jeopardy to Go to video games, RL algorithms have made it possible for computer players to achieve world-class performance and have even influenced the strategies of the best human players. Robot motor skill learning: RL enables robots to learn autonomously through trial and error how to carry out intricate tasks. Microprocessor layout and circuit design: RL systems make decisions for composing components that make up computer chips Personalized recommendations: Online services like Netflix and YouTube rely on RL techniques to tailor recommendations. Autonomous vehicles: RL models help self-driving cars learn how to navigate complex traffic environments. Supply chain optimization: RL-enabled systems learn what items need to be stored where so that customers can receive goods quickly and cheaply. Algorithm design: Researchers have broken new ground and solved long-standing problems with the help of RL systems. Breakthroughs in RL have fueled a multibillion-dollar industry, with major companies like DeepMind and OpenAI relying on RL as a core technology. Additionally, many major tech firms now have dedicated RL research teams. It is also recognized as a core topic of study. For example, RL was added to the Computer Science Standards of Learning for Virginia Public Schools earlier this year. Bridging AI and neuroscience The influence of Barto and Sutton's work extends far beyond computer science and AI, forging crucial connections between RL and brain sciences, including cognitive science, psychology and neuroscience. Their research has provided groundbreaking insights into how learning can occur, both in machines and in the human brain. One of their earliest breakthroughs came in 1981 when they showed that temporal difference (TD) learning could explain certain learning behaviors that the existing Rescorla-Wagner model couldn't. This discovery opened the door to a new way of understanding how learning happens. Building on this idea, a 1995 study found a connection between the TD algorithm and how dopamine neurons in the brain behave. This insight laid the groundwork for later experiments that confirmed that TD learning accurately describes how dopamine influences reward-based learning. With the 2025 A.M. Turing Award recognizing Barto and Sutton's lifetime achievements, their legacy underscores the importance of sustained federal investment in basic research — the kind of support that has fueled AI's breakthroughs over the last four decades.

The manufacturing technique known as 3D printing, now being used everywhere, from aircraft manufacturers to public libraries, has never been more affordable or accessible. Biomedical engineering has particularly benefited from 3D printing as prosthetic devices can be produced and tested more rapidly than ever before. However, 3D printing still faces challenges when printing living tissues, partly due to their complexity and fragility. Now, with support from the U.S. National Science Foundation, a research team at Boston University (BU) and the Wyss Institute at Harvard University has pioneered the use of gallium, a metal that can be molded at room temperature, to create tissue structures in various shapes and sizes. This innovative approach to fabrication, engineered sacrificial capillary pumps for evacuation (ESCAPE), was highlighted in a recent study published in Nature, where the team used gallium casts to mold biomaterials. The scaffolds left behind by these casts are then filled with cells cultured to form tissue structures. Vascular structures were some of the first produced using ESCAPE, particularly because of the challenges faced due to blood vessel complexity. Few techniques exist to build large (millimeter-scale) and small (micrometer-scale) structures in scaffolds made of natural materials, making this multiscale fabrication capability a novel approach. "ESCAPE can be used on several tissue architectures, but we started with vascular forms because blood vessel networks feature many different length scales," said Christopher Chen, director of BU's Biological Design Center and senior author on the study. Chen is also the deputy director of CELL-MET, an NSF Engineering Research Center at BU funded by a $34 million award from NSF, and co-principal investigator on the award for the NSF Science and Technology Center for Engineering MechanoBiology at the University of Pennsylvania. "Our blood vessel demonstrations include trees with many branches, including dead ends and portions that experience fluid flow. This allows us to model a range of healthy structures as well as diseased abnormalities." Following the success of reproducing capillary structures, researchers are hopeful these methods can be used to generate distinct tissue structures found in organs. The reliability of these ESCAPE designs will also be tested using computational modeling, further expanding the types of material reproduced using the process. Credit: Subramanian Sundaram, Boston University and Wyss Institute, Harvard University A metallic (gallium) cast used to model networks of blood vessels and lymphatic vessels that come in close proximity but not in direct contact. The gallium structure is used as a sacrificial cast to mold soft materials into complex structures in the ESCAPE process. "CELL-MET allows engineers, student trainees and medical professionals and their patients to collaborate across a broad innovation ecosystem," said Randy Duran, the lead NSF program director for the CELL-MET award. "Using systems engineering, the team has developed a novel method of fabricating structures such as blood vessels that must be produced at scales ranging from microscopic capillaries to much larger blood vessels, all within centimeter-scale heart patches that will have a broad impact on human health."

U.S. National Science Foundation-supported researchers published new findings suggesting a location where the Yellowstone Caldera could erupt, hundreds of thousands of years from now. The Yellowstone Caldera is one of the largest volcanic systems on Earth. It lurks beneath Yellowstone National Park and touches three states: Idaho, Wyoming and Montana. Over the past two million years, the volcano significantly erupted three times, leaving behind calderas, or massive craters. To better understand future eruptions, Ninfa Bennington, a volcanic seismologist with the U.S. Geological Survey, used magnetotelluric methods to identify four pots of magma stored underneath the Yellowstone Caldera. Magnetotelluric instruments help scientists identify materials that can conduct electricity beneath Earth's crust. The team used those instruments at over 100 measuring stations across the caldera to identify magma, which has a much higher conductivity than solid rocks. Of the four magma-rich regions the team discovered, only the northeastern one will remain hot enough to keep magma liquid on a long-term scale and eventually erupt. Previous major eruptions took place in different locations across the caldera.

NSF–DOE Vera C. Rubin Observatory, jointly funded by the U.S. National Science Foundation and the U.S. Department of Energy's Office of Science, will soon begin scanning the Southern Hemisphere sky every night for 10 years. Among the trillions of cosmic events and objects it will capture will be millions of exploding stars called Type Ia supernovas. These supernovas are produced by exploding white dwarf stars and are some of the brightest cosmic spectacles. They are particularly useful to researchers because they provide a sort of reliable cosmic yardstick that can be used to accurately measure vast distances in the universe. With enough observations of Type Ia supernovas, scientists can measure the universe’s expansion rate and whether it changes over time. Every time NSF-DOE Rubin Observatory detects a change in brightness or position of an object, it will send an alert to the science community. With such rapid detection, Rubin will be the most powerful tool yet for spotting Type Ia supernovas before they fade away. Observations of Type Ia supernovas were used to discover the mysterious phenomenon known as dark energy, thought to be causing the universe to expand faster than expected. In just its first few months of operation, Rubin Observatory will discover many more Type Ia supernovas than were used in the initial discovery of dark energy in the 1990s. The observatory will reveal a much larger set of the supernovas across the universe, allowing scientists to refine our existing map of space and time and create a fuller picture of dark energy’s influence. Current measurements suggest that dark energy might change over time. Understanding the nature of dark energy will in turn refine understanding of the universe's age and evolution, including when stars and galaxies first formed.

NSF Director Sethuraman Panchanathan spent the week reinforcing the agency's mission to inspire and harness talent everywhere to catalyze the progress of innovation. On Monday, Jan. 13, Panchanathan welcomed the Government of Canada's Chief Science Advisor Mona Nemer to agency headquarters, where they explored opportunities to sync global talent to advance cutting-edge research and underscored the importance of supporting societally relevant and use-inspired research to promote global prosperity. NSF has supported U.S. researchers working with Canadian counterparts in areas such as artificial intelligence, quantum information science, the bioeconomy and energy and resilience. Credit: Charlotte Geary/NSF On January 13, 2025, NSF Director Sethuraman Panchanathan met with Dr. Mona Nemer, Chief Science Advisor of Canada at NSF Headquarters. On Tuesday, Jan. 14, the director met with Rep. Brian Babin (R-TX-36), Chairman of the House Committee on Science, Space, and Technology, where he expressed his excitement for future collaborative efforts between NSF, the committee and the 119th Congress to ensure the U.S. remains at the vanguard of discovery and innovation. Later that day, he met with Rep. Jay Obernolte (R-CA-23), who chaired the House Bipartisan Task Force on Artificial Intelligence and thanked Obernolte for his task force leadership and expressed his great appreciation for the task force's recognition of NSF's longstanding AI investments and the important advancements those sustained investments have enabled. Credit: Chris Hillesheim/NSF On January 14, 2025, NSF Director Sethuraman Panchanathan met with Rep. Jay Obernolte (R-CA-23). This week, NSF is honoring STEM educators, mentors and early-career researchers advancing the frontiers of science and engineering with prestigious awards. These awards include the Presidential Awards for Excellence in Mathematics and Science Teaching, the Presidential Awards for Excellence in Science, Mathematics and Engineering Mentoring, and the Presidential Early Career Award for Scientists and Engineers. "These honorees embody the excellence and innovation that drive STEM education and research forward," said the Director. "We are proud to support these educators and scientists whose transformative work inspires students, cultivates a passion for learning and advances the frontiers of discovery." This week, Texascale Magazine also highlighted the upcoming NSF Leadership-Class Computing Facility (NSF LCCF) Horizon, led by the Texas Advanced Computing Center (TACC) at The University of Texas at Austin. "NSF LCCF represents a pivotal step forward in our mission to support transformative research across all fields of science and engineering," said the Director. "This facility will provide the computational resources necessary to address some of the most pressing challenges of our time, enabling researchers to push the boundaries of what is possible."

The U.S. National Science Foundation honors individuals recognized by the president of the United States with prestigious White House awards. These include the Presidential Awards for Excellence in Mathematics and Science Teaching (PAEMST), the Presidential Awards for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM) and the Presidential Early Career Award for Scientists and Engineers (PECASE). Together, these awards highlight exceptional K-12 STEM educators, mentors and early-career researchers advancing the frontiers of science and engineering. "These honorees embody the excellence and innovation that drive STEM education and research forward," said NSF Director Sethuraman Panchanathan. "We are proud to support these educators and scientists whose transformative work inspires students, cultivates a passion for learning and advances the frontiers of discovery." The PAEMST and PAESMEM programs, supported by NSF, highlight excellence in STEM education and mentorship. PAEMST recognizes K-12 educators who excel at engaging students in STEM learning and inspiring them to pursue careers in these fields. PAESMEM honors mentors who have enhanced participation among individuals, including those with disabilities, who may not have previously considered or had access to opportunities in STEM fields and careers. Among the nearly 400 recipients of the prestigious PECASE award, which recognizes outstanding early-career scientists and engineers, 111 have received support through the NSF CAREER program. Notably, two of this year's PECASE honorees, William Anderegg and Melanie Matchett-Wood, are former winners of the NSF Alan T. Waterman Award, underscoring their exceptional contributions to science and engineering. For more information about these awards, visit PAEMST, PAESMEM and PECASE.

A U.S. National Science Foundation-supported team recently solved an enduring physics enigma, revealing new information about how X-rays form during thunderstorms. Starting in the 1960s, scientists noticed a strange occurrence. When they performed laboratory experiments to replicate lightning and similar phenomena, they noticed that electrons accelerating between two electrodes were sometimes more energetic than expected. When researchers ran tests, they noted that the excess energy was released as sparks, which they recorded as bursts of X-rays. To solve this mystery, Victor Pasko, a professor at Penn State University, and his team used mathematical modeling to discover that during the lightning experiments, electrons interacted with the first electrode material, emitting X-rays made of photons. Some of these photons moved backward, releasing more electrons from the second electrode. This caused a repeating chain reaction; it became a feedback loop capable of producing more energetic electrons. "Our findings help explain the processes that can produce X-rays right before lighting strikes," Pasko said. "These processes had mysteriously remained radio silent and optically dark." New knowledge on X-rays also informs fields like pollution control and plasma-assisted combustion. "Our work could stimulate new research on the production of energetic electrons from solid materials, which would help researchers design innovative medical imaging devices that use X-rays," Pasko said.

U.S. National Science Foundation-supported research shows that caribou will optimize their migration path based on their collective memories. Caribou are the largest species on land in the Arctic. They are not only an important part of the ecology but are also a primary source of food for hundreds of communities. The antlered deer migrate more miles than any other land-based animal but don't always take the same path each year. To figure out how and why caribou migrate during the winter, Eliezer Gurarie, a professor at the State University of New York, and fellow researchers teamed up with the National Park Service, which had put trackable collars on over 300 female caribou in the Western Arctic Caribou Herd. The team tracked the herd's movements and deaths as it traveled across a region spanning over 360,000 square kilometers in northwest Alaska for 11 years, from 2009 to 2020. The researchers discovered that when the animals wintered south of the Kobuk River, they were more likely to survive a warm, windier winter. When they wintered north of the same river, they were more likely to survive when there was more snow and less wind. The caribou decided whether to cross the river each year as an adaptive measure to maximize their chances of survival. "A dead animal doesn't remember anything (or move again) by definition," Gurarie said. "But the general conditions that led to poor survival are certainly remembered by the other caribou." The long-term study revealed that caribou can not only understand risk but also use their knowledge to collectively make decisions that minimize risks for the herd. "This is a pretty clear and dramatic example of the concrete importance of social memory in predicting animal movements," Gurarie said. This adaptive behavior could be especially important for the species as the Arctic undergoes some of the most rapid warming on Earth.