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LZ Sees Surprising Result in Search for Dark Matter
Key Takeaways Dark matter is the mysterious substance that accounts for the vast majority of matter in our universe. LUX-ZEPLIN (LZ) is a world-leading dark matter detector and specializes in searching for WIMPs, or weakly interacting massive particles. A new LZ study found one particle interaction that is difficult to explain with known background processes and could potentially have been caused by a WIMP. LZ continues to run to see if additional data strengthens or weakens the hint of a dark matter interaction. For the better part of a century, people have been trying to understand dark matter. This invisible substance makes up roughly 85% of the mass in the universe but has never been directly detected. Determining exactly what it is remains one of the biggest questions about our world. Now, a new analysis from the LUX-ZEPLIN (LZ) experiment has recorded a single particle interaction that researchers have great difficulty explaining with known background signals from normal matter. The result does not yet meet the statistical threshold required to claim a discovery, but is the most compelling hint of dark matter reported by the experiment to date. LZ is an international collaboration of 250 scientists and engineers from 39 institutions. The detector is managed by the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and operates nearly one mile below ground at the Sanford Underground Research Facility (SURF) in South Dakota. The experiment uses 10 tonnes of ultrapure liquid xenon to search for dark matter and is optimized to look for WIMPs, or weakly interacting massive particles. The results were presented in a scientific talk at the 2026 TeV Particle Astrophysics conference in Japan. The paper will be released on the online repository arXiv and submitted to the journal Physical Review Letters. “We’re very intrigued to see this event in the data, in the region where we expect dark matter to show up and the competing backgrounds are very low,” said Rick Gaitskell, a professor at Brown University and the spokesperson for LZ. “With only one event, we don’t want to get ahead of ourselves. We are not claiming to have seen dark matter. But we have seen something interesting that we want to share with the scientific community for their input.” The LZ collaboration studies experimental data in batches. In the new result, researchers analyzed 220 live days of data collected between March 2023 and April 2024. The collaboration had previously searched this dataset for faint signals from the simplest kinds of WIMP interactions. The new analysis searched for a broader range of possible WIMP interactions that could deposit more energy in the detector. LZ is particularly sensitive to such signals while also minimizing false positives. “This was a detailed study in a region we hadn’t explored within this dataset, and we spent months of additional effort to understand all the possible causes of background events,” said Sam Eriksen, a senior research associate at the University of Bristol in the U.K. and lead author of the study. “We understand our detector and the backgrounds so well that even a single outstanding event, like the one we found, is important. We expect dark matter events to be extremely rare, so only a handful could mark the first detection of WIMP dark matter.” If the anomalous event was caused by dark matter, the WIMP that generated it would likely have a mass of at least 200 GeV/c2 (gigaelectronvolts), or more than 200 times the mass of a proton. It would also suggest a specific type of interaction between WIMPs and ordinary matter beyond the simplest model. The LZ results have not reached “5-sigma” significance, the statistical threshold considered a discovery in physics. The new analysis is 2.6 sigma, meaning there is approximately a 0.5% chance that the event could be explained by known backgrounds. With additional data, researchers can test whether the finding continues to grow in significance or fades away. LZ has already accumulated the world’s largest dark matter dataset and will continue to accrue WIMP search data at SURF, substantially improving their search statistics. LZ searches for dark matter by looking for signature flashes of light from energy deposited in the detector. The collaboration leverages multiple methods to prevent or account for particle interactions caused by normal matter. This includes the mile of rock that shields the detector from cosmic rays from space, a water tank and outer detectors that protect the central detector from background neutrons, and a suite of computational tools that disentangle particle interactions and reject dark matter mimics. “Outlier events in the data are not unexpected, but they usually stand out as a background of some kind when you look at them deeper,” said Aaron Manalaysay, a physicist at Berkeley Lab and the chair of LZ’s Institutional Board. “This is the first example in any experiment I’ve worked on of an outlier that appears valid in every way. Of course, we’re still twisting our brains trying to think if there’s a rare background mechanism we could’ve missed, but it’s thrilling to wonder if this could be the first hint of a dark-matter observation.” LZ is supported by the U.S. Department of Energy, Office of Science, Office of High Energy and Nuclear Physics, and the National Energy Research Scientific Computing Center, a DOE Office of Science user facility. LZ is also supported by the Science & Technology Facilities Council of the United Kingdom; the Portuguese Foundation for Science and Technology; the Swiss National Science Foundation; the Australian Research Council Centre of Excellence for Dark Matter Particle Physics; and the Institute for Basic Science, Korea. Thirty-nine institutions of higher education and advanced research provided support to LZ. The LZ collaboration acknowledges the assistance of the Sanford Underground Research Facility. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.
Scientists Release Biggest 2D Map of the Universe
Key Takeaways The DESI Legacy Imaging Surveys combined more than 263,000 telescope exposures to make the largest 2D map of the universe in visible and near-infrared light. Astronomers can pair the Legacy Surveys map with their own observations to explore our universe and search for rare phenomena. The 2D map serves as the foundation for the Dark Energy Spectroscopic Instrument survey to measure the universe in 3D and investigate dark energy. Hold on to your telescopes: the DESI Legacy Imaging Surveys team has released the largest-ever 2D color map of the universe. The 5.6-trillion-pixel map contains nearly 4 billion celestial objects, primarily stars and galaxies. The data is available for all to use and publicly viewable through the Legacy Survey Sky Viewer. Astronomers and citizen scientists can explore the map or combine it with their own observations to better understand our universe. Researchers can search for rare phenomena like gravitational lenses, observe fleeting events like supernovae, and investigate two of physics’ biggest mysteries: dark matter, the invisible substance that accounts for most of the mass in our universe, and dark energy, the force driving our universe’s accelerating expansion. The new map builds on earlier versions from the DESI Legacy Imaging Surveys that have already proved invaluable. To date, more than 1,800 science papers that reference the Legacy Surveys data have been published. “It’s part of the fabric of astronomy research now,” said David Schlegel, a co-lead of the Legacy Surveys and scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). “When you’re working with astronomical objects today, you often start by pulling up the Legacy Imaging Viewer to see what you’re looking at.” Covering roughly 75% of the sky in visible and near-infrared light, the updated map provides a deep view of the extragalactic universe not blocked by the dust and stars of our own Milky Way. Researchers expect it will remain the most comprehensive 2D map of our universe for years to come. More than 160 scientists contributed to data collection for the project, and a team of 20 produced the final dataset released today. It was built by combining 263,407 telescope exposures from three ground-based sky surveys: the Dark Energy Camera Legacy Survey (DECaLS) at NSF Cerro Tololo Inter-American Observatory, the Mayall z-band Legacy Survey (MzLS) at NSF Kitt Peak National Observatory, and the Beijing-Arizona Sky Survey (BASS) at the University of Arizona’s Steward Observatory. That was supplemented by years of data from NASA’s Wide-field Infrared Survey Explorer (WISE) satellite mission and additional public data. “For our team, these data are fundamental to our investigation of the expansion history of the universe and the formation of our galaxy,” said Arjun Dey, co-lead of the Legacy Surveys and an astronomer at NSF NOIRLab. “But the skies belong to everyone, and this survey gives everyone the chance to explore the sky and marvel at its wonders.” Here be galaxies The DESI Legacy Imaging Surveys were originally conducted to prepare for the Dark Energy Spectroscopic Instrument (DESI) survey. The Legacy Surveys’ 2D map is essentially a deep photograph of the sky; it records where galaxies and stars appear and how bright they appear. This crucial step enables DESI to select objects and measure their light in different wavelengths to determine their distances, building the largest high-resolution 3D map ever made. Scientists study the way galaxies have clustered at different ages of the universe to track dark energy over time. In April 2026, DESI completed its original five-year survey ahead of schedule and with vastly more objects than expected. The early results have shown surprising hints that dark energy’s impact may be weakening over time — a paradigm shift that could potentially shape the predicted fate of our universe. DESI expects to publish improved results using their first five years of data in 2027 and is continuing observations into 2028. DESI was so efficient at observing galaxies, the Legacy Surveys map needed to expand. Early on, “it became clear we might run out of galaxies to look at and run out of sky, and we better start doing something about that,” said Schlegel, who also works on DESI. The new Legacy Surveys map has been used to select DESI targets since June 2026 and will guide the telescope’s operations over the coming years. Computing the cosmos Merging hundreds of thousands of images taken on 2,285 nights, each with unique atmospheric and telescope conditions, was a massive computational effort. It took about a year to develop the computer code and eight weeks to process all the images at the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab. Beyond supporting DESI, the Legacy Surveys will be a foundational reference for the next generation of telescopes. As new observatories like the NSF-DOE Vera C. Rubin Observatory (jointly funded by NSF and DOE’s Office of Science) and NASA’s Nancy Grace Roman Space Telescope come online, researchers can compare their observations with one of the deepest and most comprehensive views of the sky ever assembled. The Legacy Surveys data will also help scientists train artificial intelligence tools to analyze petabytes of astronomical data and accelerate new discoveries. It will be among the datasets used in an astrophysics pilot project within the American Science Cloud, part of the DOE’s Genesis Mission. The DESI Legacy Imaging Surveys are supported by the U.S. Department of Energy’s Office of High Energy Physics; the National Energy Research Scientific Computing Center, a DOE Office of Science user facility; the U.S. National Science Foundation, Division of Astronomical Sciences; and the partner institutions. DESI is supported by the DOE Office of Science and NERSC. Additional support for DESI is provided by the NSF; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI) of Mexico; the Ministry of Science and Innovation of Spain; and by the DESI member institutions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.
When It Comes to Fusion, Materials Matter
Fusion at high temperatures powers the sun and, if harnessed, could provide a potential source of energy here on Earth. But controlling fusion reactions has other benefits. The process also generates subatomic particles called neutrons that are used in a range of applications spanning medicine, research, and national security. Now, scientists at the University of California, Davis (UC Davis), and the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have found that the materials surrounding a fusion reaction can dramatically increase how often it occurs, particularly at low energies where fusion is rare. The study was published July 18 in Nature Communications. Their approach sets up a way to study and engineer nuclear reactions within solid materials, opening a new field of “materials-driven fusion.” Instead of designing materials just to survive the harsh conditions of fusion, researchers might be able to design materials that boost the reaction in specific conditions, similar to the way catalysts speed up chemical processes. “It gives you a new knob to turn that you didn’t have before,” said Arun Persaud, head of the Fusion Science & Ion Beam Technology group in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division. “If we understand this effect better, it opens the door to engineering new materials that would affect the fusion rate under certain conditions. Someday future progress might enable more compact and efficient neutron generators, which have all kinds of applications, like cargo screening, planetary science, and medical therapy and imaging.” In the experiment, researchers used two different methods to pack deuterium, a heavy form of hydrogen atoms that is often used in fusion, into thin foils of palladium and titanium. They then fired a beam of deuterium ions at the foils at different energies and measured how often fusion happened. Then they compared the rates from the different materials and methods with the “bare” fusion reaction (not in a material). The team found that fusion rates depended on how the deuterium was loaded into the metal foils. The biggest effect was at the lowest energies, below 2.5 kiloelectronvolts (keV), where theory predicts fusion rates drop off sharply. Instead, researchers found a surprising plateau: Some samples showed elevated fusion rates roughly a quintillion times higher than bare fusion reactions. (A quintillion is a 1 followed by 18 zeroes.) Researchers are not exactly sure why that’s happening, though they have some ideas and ways to test them. The electrons and defects within the material might partially shield repulsive electrostatic forces between deuterium nuclei, making it easier for them to get close together and fuse. Tuning the electronic structure, defects, and composition of materials could help make nuclear reactions happen more readily. “It comes down to better understanding the mechanism so that we can try to enhance it,” said corresponding author Jeremy Munday, a professor at UC Davis. “We’ve seen that we can increase fusion rates, but what is the limit? Can we bring it to lower temperatures or energies? There’s obviously a lot of interest and excitement about fusion, so if there’s something we can learn about the physics at these lower energy scales, maybe there’s something we can translate to other areas of nuclear science.” The team plans to explore a wider range of materials and continue probing the unexpected fusion plateau at lower energies. Their work establishes a reproducible experimental platform to study how solid materials influence nuclear reactions, creating a new area of research that links fusion science with materials science and chemistry. “The work shows conclusively that the material environment where fusion occurs at low temperatures is an active participant rather than a passive container,” said Cameron Geddes, director of ATAP. “That adds a new dimension to fusion research.” This work was funded by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E). ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.
The National Academy of Sciences Elects Two Berkeley Lab Researchers
Two Lawrence Berkeley National Laboratory (Berkeley Lab) scientists have been elected to the National Academy of Sciences (NAS) in recognition of their distinguished and continuing achievements in original research. They are part of a cohort of 120 new members and 25 international members. The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — alongside the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. The 2026 electees bring the total number of active members to 2,705 with 557 international members. Gary Karpen is a biologist senior faculty scientist in Berkeley Lab’s Biological Systems and Engineering Division and a Professor of Cell and Developmental Physiology in the Department of Molecular and Cell Biology at UC Berkeley. Karpen studies how the structure, organization, and maintenance of genetic material impact inheritance and cell function. His lab focuses on chromatin — a packaged form of DNA strands wrapped around proteins that comprises our chromosomes — with a particular emphasis on centromeres, rarely expressed sequences called heterochromatin, DNA repair, and regulatory RNAs. His research reveals insights into fundamental aspects of nuclear organization and genome regulation that are shared across the tree of life, broadening our understanding of cancer and other diseases, birth defects, and aging. Kam-Biu Luk is a Professor Emeritus of physics at UC Berkeley and a retired senior faculty scientist in Berkeley Lab’s Physics Division. He is an experimental particle physicist whose research has focused on neutrinos: nearly massless, electrically neutral elementary particles that can pass through matter virtually undetected. Neutrinos are created during radioactive decay processes, including the fusion process occurring in stars and the fission process that powers nuclear reactors. Luk initiated and co-led the Daya Bay Reactor Neutrino Experiment, a project based in China with more than 200 international collaborators. In 2012, the Daya Bay experiment discovered a new form of a phenomenon called “neutrino oscillation” in which neutrinos change from one type to another. For this discovery, Luk and his collaborator Yifan Wang received the 2014 American Physical Society Division of Particles and Fields W.K.H. Panofsky Prize for outstanding work in experimental particle physics, and he was also the co-recipient of the 2016 Breakthrough Prize. Luk is currently participating in the Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory, which will investigate any potential difference in behavior between neutrinos and antineutrinos that may shed light on the evolution of the universe, especially why antimatter is absent in the present universe. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.
UC Names Katherine Yelick to Head Lawrence Berkeley National Laboratory
Yelick is a distinguished academic and national lab leader with deep expertise in artificial intelligence and high-performance computing
Five Berkeley Lab Scientists Receive DOE Early Career Research Awards
Five scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the Department of Energy. The ECRP program, now in its 16th year, was established to bolster the nation’s scientific workforce by supporting exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities. To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years. This year’s Berkeley Lab awardees and their projects are listed below: Timon Heim: Better detectors for more powerful particle colliders Timon Heim is a staff scientist in the Physics Division. His ECRP project will develop a new type of detector that can track the particles generated by collisions in particle colliders with high spatial and temporal resolution. Current detectors in use at high-energy colliders, like the ATLAS detector at the Large Hadron Collider (LHC) at CERN, can track the trajectory and measure momentum of particles, but they can’t track a particle’s time of arrival with sufficient precision to discern from which of the many particle interactions they originate. Heim’s proposed technology — called a 4D tracking detector, because it captures data about particles’ positions in space and time — aims to solve this challenge, allowing researchers to glean more precise data from collision experiments to uncover new insights into fundamental forces and potentially discover new particles. The enhanced capabilities of 4D detectors will be a necessity for experiments at future colliders, such as the Muon Collider or Future Circular Collider. The project will develop custom microelectronics that process the signal of a pixelated silicon sensor that can register charged particles passing through it in an extreme radiation environment that these colliders produce during operation. The goal is to measure individual particles’ position with 50-micro-meter spatial and 50 pico-second (one trillionth of a second) temporal precision, for an incoming flux of around 1 billion particles per second per square centimeter. Harrison Lisabeth: Studying underground processes to unleash abundant energy Harrison Lisabeth is a research scientist in the Energy Geoscience Division focused on combining real-world experiments and AI to guide development of geothermal energy systems. His project will explore how deep underground rock formations – where geothermal wells are dug – are stressed by chemical reactions of the minerals and mechanical strain from movements in the Earth. A better understanding of subsurface stresses is essential to engineer safe and reliable geothermal wells that access the heat of deep rocks to produce electricity. It is estimated that the U.S. could generate ten times the amount of energy needed to power the grid using geothermal sources. Lisabeth plans to simulate how minerals grow, dissolve, and influence the behavior of surrounding rock in geothermal reservoirs by tracking chemical changes in rock samples under pressure in the lab. The project will also develop machine learning models to predict geologic strain based on field measurements and refine existing models that describe how common rock types deform under stress. Combining these approaches, Lisabeth hopes to develop a framework to predict stress to improve stability and longevity of geothermal wells. Daniel Carney: Pushing the quantum boundaries of particle measurement Daniel Carney is a staff scientist in the Physics Division who uses quantum information science to study particle physics and gravity. At very small scales, “quantum noise” — random fluctuations from the act of measurement — can drown out faint signals from rare processes. Carney’s ECRP project, “Fundamental Physics at the Quantum Limits of Measurement,” aims to build a new mechanical quantum sensor that can measure extremely small particle interactions below the limits of that quantum noise. The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. 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. For more information, please visit energy.gov/science.