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  • richardmitnick 10:27 am on May 12, 2020 Permalink | Reply
    Tags: , , , LBNL, , , ,   

    From Lawrence Berkeley National Lab: “Berkeley Lab COVID-19 related research and additional information. News Center CUORE Underground Experiment in Italy Carries on Despite Pandemic” 


    From Lawrence Berkeley National Lab

    May 12, 2020
    Glenn Roberts Jr.
    (510) 520-0843
    geroberts@lbl.gov

    Laura Marini, a postdoctoral researcher at UC Berkeley and a Berkeley Lab affiliate who serves as a run coordinator for the underground CUORE experiment, shares her experiences of working on CUORE and living near Gran Sasso during the COVID-19 pandemic. (Credit: Marilyn Sargent/Berkeley Lab)

    Note: This is the first part in a recurring series highlighting Berkeley Lab’s ongoing work in international physics collaborations during the pandemic.

    As the COVID-19 outbreak took hold in Italy, researchers working on a nuclear physics experiment called CUORE at an underground laboratory in central Italy scrambled to keep the ultrasensitive experiment running and launch new tools and rules for remote operations.

    This Cryogenic Underground Observatory for Rare Events experiment – designed to find a never-before-seen process involving ghostly particles known as neutrinos, to explain why matter won out over antimatter in our universe, and to also hunt for signs of mysterious dark matter – is carrying on with its data-taking uninterrupted while some other projects and experiments around the globe have been put on hold.

    Finding evidence for these rare processes requires long periods of data collection – and a lot of patience. CUORE has been collecting data since May 2017, and after upgrade efforts in 2018 and 2019 the experiment has been running continuously.

    Before the pandemic hit there were already tools in place that stabilized the extreme cooling required for CUORE’s detectors and provided some remote controls and monitoring of CUORE systems, noted Yury Kolomensky, senior faculty scientist at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and the U.S. spokesperson for CUORE.

    The rapid global spread of the disease, and related restrictions on access to the CUORE experiment at Gran Sasso National Laboratory (Laboratori Nazionali del Gran Sasso, or LNGS, operated by the Italian Nuclear Physics Institute, INFN) in central Italy, prompted CUORE leadership and researchers – working in three continents – to act quickly to ramp up the remote controls to prepare for an extended period with only limited access to the experiment.

    CUORE experiment,at the Italian National Institute for Nuclear Physics’ (INFN’s) Gran Sasso National Laboratories (LNGS) in located in the Abruzzo region of central Italy,a search for neutrinoless double beta decay

    Gran Sasso LABORATORI NAZIONALI del GRAN SASSO, located in the Abruzzo region of central Italy

    Just days before the new restrictions went into effect at Gran Sasso, CUORE leadership on March 4 made the decision to rapidly deploy a new remote system and to work out the details of how to best maintain the experiment with limited staffing and with researchers monitoring in different time zones. The new system was fully operational about a week later, and researchers at Berkeley Lab played a role in rolling it out.

    “We were already planning to transition to remote shift operations, whereby a scientist at a home institution would monitor the systems in real time, respond to alarms, and call on-site and on-call personnel in case an emergency intervention is needed,” Kolomensky said, adding, “We were commissioning the system at the time of the outbreak.”

    Brad Welliver, a postdoctoral researcher, served as Berkeley Lab’s lead developer for the new remote monitoring system, and Berkeley Lab staff scientist Brian Fujikawa was the overall project lead for the enhanced remote controls, collectively known as CORC, for CUORE Online/Offline Run Check.

    Fujikawa tested controls for starting and stopping the data collection process, and also performed other electronics testing for the experiment from his home in the San Francisco Bay Area.

    He noted that the system is programmed to send email and voice alarms to the designated on-shift CUORE researcher if something is awry with any CUORE system. “This alarm system is particularly important when operating CUORE remotely,” he said, as in some cases on-site workers may need to visit the experiment promptly to perform repairs or other needed work.

    Development of so-called “slow controls,” which allow researchers to monitor and control CUORE equipment such as pumps and sensors, was led by Joe Johnston at the Massachusetts Institute of Technology.

    “Now we can perform most of the operations from 6,000 miles away,” Kolomensky said.

    And many participants across the collaboration continue to play meaningful roles in the experiment from their homes, from analyzing data and writing papers to participating in long-term planning and remote meetings.

    Despite access restrictions at Gran Sasso, experiments are still accessible for necessary work and checkups. The laboratory remains open in a limited way, and its staff still maintains all of its needed services and equipment, from shuttles to computing services.

    Laura Marini, a postdoctoral researcher at UC Berkeley who serves as a run coordinator for CUORE and is now living near Gran Sasso, is among a handful of CUORE researchers who still routinely visits the lab site.

    “As a run coordinator, I need to make sure that the experiment works fine and the data quality is good,” she said. “Before the pandemic spread, I was going underground maybe not every day, but at least a few times a week.” Now, it can be about once every two weeks.

    Sometimes she is there to carry out simple fixes, like a stuck computer that needs to be restarted, she said. Now, in addition to the requisite hard hat and heavy shoes, Marini – like so many others around the globe who are continuing to work – must wear a mask and gloves to guard against the spread of COVID-19.

    The simple act of driving into the lab site can be complicated, too, she said. “The other day, I had to go underground and the police stopped me. So I had to fill in a paper to declare why I was going underground, the fact that it was needed, and that I was not just wandering around by car,” she said. Restrictions in Italy prevent most types of travel.

    2
    Laura Marini now wears a protective mask and gloves, in addition to a hard hat, during her visits to the CUORE experiment site. (Credit: Gran Sasso National Laboratory – INFN)

    CUORE researchers note that they are fortunate the experiment was already in a state of steady data-taking when the pandemic hit. “There is no need for continuous intervention,” Marini said. “We can do most of our checks by remote.”

    She said she is grateful to be part of an international team that has “worked together on a common goal and continues to do so” despite the present-day challenges.

    Kolomensky noted some of the regular maintenance and upgrades planned for CUORE will be put off as a result of the shelter-in-place restrictions, though there also appears to be an odd benefit of the reduced activity at the Gran Sasso site. “We see an overall reduction in the detector noise, which we attribute to a significantly lower level of activity at the underground lab and less traffic in the highway tunnel,” he said. Researchers are working to verify this.

    CUORE already had systems in place to individually and remotely monitor data-taking by each of the experiment’s 988 detectors. Benjamin Schmidt, a Berkeley Lab postdoctoral researcher, had even developed software that automatically flags periods of “noisy” or poor data-taking captured by CUORE’s array of detectors.

    Kolomensky noted that work on the CORC remote tools is continuing. “As we have gained more experience and discovered issues, improvements and bug fixes have been implemented, and these efforts are still ongoing,” he said.

    CUORE is supported by the U.S. Department of Energy Office of Science, Italy’s National Institute of Nuclear Physics (Instituto Nazionale di Fisica Nucleare, or INFN), and the National Science Foundation (NSF). CUORE collaboration members include: INFN, University of Bologna, University of Genoa, University of Milano-Bicocca, and Sapienza University in Italy; California Polytechnic State University, San Luis Obispo; Berkeley Lab; Lawrence Livermore National Laboratory; Massachusetts Institute of Technology; University of California, Berkeley; University of California, Los Angeles; University of South Carolina; Virginia Polytechnic Institute and State University; and Yale University in the US; Saclay Nuclear Research Center (CEA) and the Irène Joliot-Curie Laboratory (CNRS/IN2P3, Paris Saclay University) in France; and Fudan University and Shanghai Jiao Tong University in China.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 9:59 am on May 12, 2020 Permalink | Reply
    Tags: , , , Kristin Persson, LBNL, , Materials Project,   

    From Lawrence Berkeley National Lab: “Making a Material World Better, Faster Now: Q&A With Materials Project Director Kristin Persson” 


    From Lawrence Berkeley National Lab

    May 8, 2020
    Theresa Duque
    (510) 424-2866
    tnduque@lbl.gov

    World-renowned computational materials scientist from Berkeley Lab and UC Berkeley looks back on a career founded in quantum mechanics, and looks ahead to faster clean-energy solutions with machine learning.

    1
    Materials Project Director Kristin Persson (Credit: Roy Kaltschmidt/Berkeley Lab)

    Kristin Persson is at the helm of a materials revolution.

    Since 2011, she has led the Materials Project, an open-access online database that virtually delivers the largest collection of materials properties to scientists from every corner of the globe who are searching for the next big thing in batteries, solar cells, and computer chips.

    Harnessing the power of supercomputers at Berkeley Lab’s National Energy Research Scientific Computing Center (NERSC), and customized machine-learning algorithms based on state-of-the art quantum mechanical theory, Persson developed the Materials Project with open-access service, accuracy, speed, and user-friendliness in mind.

    NERSC at LBNL

    NERSC Cray Cori II supercomputer, named after Gerty Cori, the first American woman to win a Nobel Prize in science

    NERSC Hopper Cray XE6 supercomputer, named after Grace Hopper, One of the first programmers of the Harvard Mark I computer

    NERSC Cray XC30 Edison supercomputer

    NERSC GPFS for Life Sciences


    The Genepool system is a cluster dedicated to the DOE Joint Genome Institute’s computing needs. Denovo is a smaller test system for Genepool that is primarily used by NERSC staff to test new system configurations and software.

    NERSC PDSF computer cluster in 2003.

    PDSF is a networked distributed computing cluster designed primarily to meet the detector simulation and data analysis requirements of physics, astrophysics and nuclear science collaborations.

    Future:

    Cray Shasta Perlmutter SC18 AMD Epyc Nvidia pre-exascale supeercomputer

    NERSC is a DOE Office of Science User Facility.

    Scientists seeking to design a better battery electrode, for example, only need to log into their free Materials Project user account. A few keystrokes here, a mouse click there, and users enter the online database’s vast, virtual catalog of most known inorganic materials and thousands more that may exist. The Materials Project narrows the 124,000 inorganic compounds, and some 35,000 molecules, down to the best candidate – without the Materials Project, that search would take months to do.

    “The Materials Project is unique in its ability to calculate a multitude of properties using high-quality first-principles calculations for materials research. With our data we can serve everyone – industry, academia, the whole world – without having to compete for profit in the private sector,” said Persson, a computational materials scientist who holds titles of senior faculty scientist in the Energy Storage & Distributed Resources Division in Berkeley Lab’s Energy Technologies Area and professor of materials science and engineering at UC Berkeley.

    “And as somebody who passionately cares about the environment, I just want to come up with the next clean-energy solution as fast as possible,” she said.

    In the Q&A below, Persson shares what inspired her to launch the Materials Project, her thoughts on the future of materials research and machine learning, and how she found her own way into a STEM (science, technology, engineering, and math) career.

    Q: What inspired you to launch the Materials Project database?

    Persson: When I was a postdoc at MIT, I was working on what’s known as density functional theory, a technique for modeling the electronic structure of materials in their ground state, or the material’s lowest energy state. At the time, DFT was still fairly new and the group I was in had just started to explore how the technique could be used in high-throughput computing, a technique that automatically runs the same analytical process simultaneously on multiple computer systems.

    Word had gotten around about our work. And in 2004, a U.S.-based battery manufacturer asked us if we could use our high-throughput computing technique – which uses multiple computers to automatically run the same process over thousands of compounds – to search for a better material for its battery’s electrode chemistry.

    In addition to funding the project, our industry partner gave us free time on their supercomputer. Having access to that much computing power really opened up a new world for me. I was comfortable with using computational DFT techniques to understand how individual materials work, but the idea of turning it around and using it on a supercomputer as an automated screening vehicle was game-changing. Suddenly you can screen hundreds of materials per day for a specific property, learn about chemistry and structural trends, and become smarter about where to look. Without a supercomputer, screening those same materials would take a team of researchers months to complete.

    The data from that project laid the foundation for the Materials Project. And when I was hired by Berkeley Lab in 2008, I brought that vision with me. During my second year here, I got funding from the Laboratory Directed Research and Development program to develop the nascent Materials Project’s capabilities and make it open access so it could serve a diverse community of materials scientists – like battery researchers, photovoltaics researchers, and researchers who specialize in data storage materials. In 2011, we launched the Project to the public and we have since continuously improved it with more materials, better search capabilities, and even more importantly, more diverse coverage of properties and analyses algorithms. Recently, and thanks to our broad and comprehensive datasets, we are adding state-of-the-art machine-learning algorithms to help researchers understand and identify functional materials.

    Today, the Materials Project is the largest materials data provider in the world, serving data more than a million times a day to more than 120,000 users all over the world, and it’s been cited by thousands of papers.

    Nobody has ever had this kind of data at their fingertips before. It’s a complete paradigm change in that sense. It’s exciting to know that researchers all over the world are publishing papers that used data from the Materials Project.

    Many of them are energy-related researchers, spanning batteries, catalysis, photovoltaics, thermoelectrics, et cetera, but I’ve been pleasantly surprised to see it used in other fields, like alloy design, scintillators, high-pressure and magnetic materials, and even astrophysics. It is extremely rewarding when people call you up and say, “Hey, a paper published in this journal said they used the Materials Project to understand the formation of concrete in space!”

    The Materials Project wouldn’t have been able to generate all that data without the support of the Basic Energy Sciences program within the Department of Energy’s Office of Science and Berkeley Lab’s supercomputers at NERSC. Similarly, many of the crucial, early software and architecture choices were made together with experts in the Computational Research Division. The interdisciplinary nature of the Project – combining domain knowledge, high-performance computing, and modern data infrastructure and dissemination, is really perfectly suited for a national lab, where you can build collaborative, long-term teams with permanent staff.

    Q: How can the Materials Project help to accelerate technological advancements for clean energy?

    Persson: The loop of materials design, synthesis, and characterization is traditionally intensely time-consuming. We hope that data-driven approaches fueled by computations can accelerate each aspect of that loop, enabling new materials for powerful rechargeable batteries for electric cars, or semiconductors that could make artificial photosynthesis a reality. With the Materials Project, clean-energy researchers can virtually test hundreds to thousands of components and then focus on the most promising candidates, use simulations and associated machine learning to accelerate the identification of new materials, and use computational insights and guidelines for optimal synthesis conditions.

    As our data grows, we are building machine-learning tools and curated datasets into the database, which saves researchers time and money so they can focus on their important work to help the world. And because we cast it in a way that any materials scientist can understand, such as phase diagrams, bandgaps, and electronic conductivity, I can see the Materials Project becoming a cornerstone in all materials scientists’ portfolio because they don’t have to become a computational expert to use this data – however, as with all data, they do need to understand its limitations and level of accuracy.

    Q: What’s your dream machine-learning materials app?

    Persson: Harnessing both experimental and computed data with on-the-fly machine learning for rapid iterations and insights. With machine learning, the fuel is the data. And researchers from both industry and academia agree that if we want to take advantage of what machine learning has to offer, we still need high-quality, diverse, curated data.

    As someone whose role is to provide that data, I’m very interested in what robotics can do for the experimental side of materials science. Robotically automated materials synthesis could help us gather high-quality, robust data by making sure that an experiment is done exactly the same way every time it’s performed. And that’s very hard to do with humans, because people are different and will perform the same task in slightly different ways.

    I am often asked if robots will replace scientists. Robots, just like the supercomputers at NERSC, are extremely powerful tools to produce data faster and more robustly. However, robots will not replace humans. They will just broaden our experience; enable us to make better, informed decisions; and help us focus on what we do best – use our amazing and creative human brain to solve the scientific and engineering problems of the day.

    Q: What’s next for the Materials Project?

    Persson: I’d like to do more industry outreach and make the Materials Project an integrated part of both the academic as well as the industrial science process. When I was a graduate student, density functional theory was a fairly young technique, so if you’re a manager at a semiconducting company and you haven’t hired anybody who completed their Ph.D. in the last 15 years, you probably don’t even know that materials databases like the Materials Project even exist.

    I’d also like to collaborate with our partners across the national laboratory system. I see the Materials Project growing into a data institute, harnessing both computed as well as standardized experimental datasets, where we not only provide large sets of machine-learning data to other labs and industry researchers but we also work directly with them so they know how to use all of the machine-learning features and simulations that the Materials Project has to offer.

    Q: When you were a child, did you dream of becoming a scientist?

    Persson: No, not really. Actually, when I was very young, I wanted to be an opera singer. I loved singing – I still do, and when I was little, opera seemed like the perfect environment for that. Then I considered becoming an archaeologist. I was drawn to archaeology because I love history and enjoy discovering how people lived – I was always fascinated by the idea of unearthing stories of people from ancient eras: what they thought, what they believed in, and how they lived day to day.

    Q: Were you always good at math?

    Persson: It depends on how far back you are asking. Between the ages of 7 and 11, I had pretty mediocre grades across the board.

    I remember a particular, standardized math test, at the age of 10, that I didn’t do well on. Feeling very disappointed and honestly nervous about my future, I started doing an hour of math a day by myself, without a tutor. I did basic math – I learned by redoing all sorts of problems wherever I could find them in textbooks just to make sure I understood what was going on.

    It wasn’t easy because no one was directing me. Instead, it was my own growing ambition and determination that drove me. By the time I was 12, I was at the head of my class in every single subject.

    Q: What led you to computational materials science?

    Persson: When I was in college I initially wanted to study medicine, but I ended up studying engineering physics, which is very broad and fast-paced. And it was during that time when I fell in love with quantum mechanics. I thought it was the most beautiful thing ever – physics suddenly made sense together with the math, and it was gorgeous.

    When I completed my master’s degree – my thesis was on neutrino oscillations, which is essentially theoretical particle physics – I was awarded a doctoral fellowship that would allow me to go wherever I wanted to go.

    After interviewing four different professors in four very different fields, I ended up choosing the computational materials group in the Theoretical Physics Department at the Royal Institute of Technology in Stockholm, Sweden, because I liked their methodology. They used simulations together with theoretical frameworks to figure out how materials work on the fundamental level of electrons and atoms.

    And that’s why I tell my graduate students, “Don’t expect that by the age of 25 you will know exactly what you want to do in life. There are so many interesting topics when you dig deeper.” And for me, it was important that I was happy with the methodology, the every-day tasks, and getting along with the people you work with.

    The Materials Project is supported by the DOE Office of Science.

    NERSC is a DOE Office of Science User Facility located at Berkeley Lab.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 1:20 pm on May 4, 2020 Permalink | Reply
    Tags: "Study: Could Dark Matter Be Hiding in Existing Data?", LBNL   

    From Lawrence Berkeley National Lab: “Study: Could Dark Matter Be Hiding in Existing Data?” 

    From Lawrence Berkeley National Lab

    May 4, 2020

    Glenn Roberts Jr.
    geroberts@lbl.gov
    (510) 520-0843

    Current experiments’ detectors and data analyses efforts could be refocused to seek out newly suggested types of dark matter signals that may have been overlooked.

    1
    This image was produced by a simulation showing the evolution of dark matter in the universe. (Credit: Milennium-II Simulation)

    Dark matter has so far defied every type of detector designed to find it. Because of its huge gravitational footprint in space, we know dark matter must make up about 85 percent of the total mass of the universe, but we don’t yet know what it’s made of.

    Several large experiments that hunt for dark matter have searched for signs of dark matter particles knocking into the atomic nuclei via a process known as scattering, which can produce tiny flashes of light and other signals in these interactions.

    Now a new study, led by researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley, suggests new paths for catching the signals of dark matter particles that have their energy absorbed by these nuclei.

    The absorption process could give an affected atom a kick that causes it to eject a lighter, energized particle such as an electron, and it might produce other types of signals, too, depending on the nature of the dark matter particle.

    The study focuses mostly on those cases where an electron or neutrino is ejected as the dark matter particle strikes an atom’s nucleus.

    Published May 4 in Physical Review Letters, the study proposes that some existing experiments, including ones that search for dark matter particles and processes related to neutrinos – ghostly, detectable particles that can pass through most matter and have the ability to change into different forms – can easily be broadened to also look for these absorption-related types of telltale dark matter signals.

    Also, the researchers propose that new searches in previously collected particle detector data could possibly turn up these overlooked dark matter signals.

    “In this field, we’ve had a certain idea in mind about well-motivated candidates for dark matter, such as the WIMP,” or weakly interacting massive particle, said Jeff Dror, the lead author of the study who is a postdoctoral researcher in Berkeley Lab’s Theory Group and UC Berkeley’s Berkeley Center for Theoretical Physics.

    Lux-Zeppelin photomultiplier tubes at SURF

    Dark matter pushes at the boundaries of the known fundamental laws of physics, encapsulated in the Standard Model of particle physics, and “The WIMP paradigm is very easy to build into the Standard Model, but we haven’t found it for a long time,” Dror noted.

    So, physicists are now considering other places that dark matter particles may be hiding, and other particle possibilities such as theorized “sterile neutrinos” that could also be brought into the family of particles known as fermions – which includes electrons, protons, and neutrinos.

    “It’s easy, with small modifications to the WIMP paradigm, to accommodate a whole different type of signal,” Dror said. “You can make a huge amount of progress with very little cost if you step back a little bit in the way we’ve been thinking about dark matter.”

    Robert McGehee, a UC Berkeley graduate student, and Gilly Elor of the University of Washington were study co-authors.

    The researchers note that the range of new signals they are focusing on opens up an “ocean” of dark matter particle possibilities – as-yet-undiscovered fermions with masses lighter than the typical range considered for WIMPs. They could be close cousins of sterile neutrinos, for example.

    The study team considered absorption processes known as “neutral current,” in which nuclei in the detector material recoil, or get jolted by their collision with dark matter particles, producing distinct energy signatures that can be picked up by the detector; and also those known as “charged current,” which can produce multiple signals as a dark matter particle strikes a nucleus, causing a recoil and the ejection of an electron.

    The charge current process can also involve nuclear decay, in which other particles are ejected from a nucleus as a sort of domino effect triggered by the dark matter absorption.

    3
    This chart shows the sensitivity range to charged current signals by a variety of experiments. (Credit: Jeff A. Dror, Gilly Elor, and Robert McGehee)

    Looking for the study’s suggested signatures of both the neutral current and charge current processes could open up “orders of magnitude of unexplored parameter space,” the researchers note. They focus on energy signals in the MeV, which means millions of electron volts. An electron volt is a measure of energy that physicists use to describe the masses of particles. Meanwhile, typical WIMP searches are now sensitive to particle interactions with energies in the keV range, or thousands of electron volts.

    For the various particle interactions the researchers explored in the study, “You can predict what is the energy spectrum of the particle coming out or the nucleon that’s getting the ‘kick,’” Dror said. Nucleon refers to the positively charged proton or uncharged neutron that resides in an atom’s nucleus and that could absorb energy when struck by a dark matter particle. These absorption signals could possibly be more common than the other types of signals that dark matter detectors are typically designed to find, he added – we just don’t know yet.

    Experiments that have large volumes of detector material, with high sensitivity and very low background “noise,” or unwanted interference from other types of particle signals, are particularly suited for this expanded search for different types of dark matter signals, Dror said.

    LUX-ZEPLIN (LZ), for example, an ultrasensitive Berkeley Lab-led dark matter search project under construction in a former South Dakota mine [SURF], is a possible candidate as it will use about 10 metric tons of liquid xenon as its detector medium and is designed to be heavily shielded from other types of particle noise.

    Already, the team of researches participating in the study has worked with the team operating the Enriched Xenon Observatory (EXO), an underground experiment searching for a theorized process known as neutrino-less double beta decay using liquid xenon, to open up its search to these other types of dark matter signals.

    And for similar types of experiments that are up and running, “The data is already basically sitting there. It’s just a matter of looking at it,” Dror said.

    The researchers name a laundry list of candidate experiments around the world that could have relevant data and search capabilities that could be used to find their target signals, including: CUORE, LZ predecessor LUX, PandaX-II, XENON1T, KamLAND-Zen, SuperKamiokande, CDMS-II, DarkSide-50, and Borexino among them.

    As a next step, the research team is hoping to work with experiment collaborations to analyze existing data, and to find out whether search parameters of active experiments can be adjusted to search for other signals.

    “I think the community is starting to become fairly aware of this,” Dror said, adding, “One of the biggest questions in the field is the nature of dark matter. We don’t know what it is made out of, but answering these questions could be within our reach in the near future. For me, that’s a huge motivation to keep pushing – there is new physics out there.”

    Researchers participating in the study received support from the U.S. Department of Energy Office of Science and the National Science Foundation’s Graduate Research Fellowship Program.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 11:12 am on March 30, 2020 Permalink | Reply
    Tags: "Using Fiber Optics to Advance Safe and Renewable Energy", Fiber optic cables it turns out can be incredibly useful scientific sensors., LBNL, Making offshore wind and natural gas storage more reliable., Offshore wind resources in the U.S. are abundant and have the potential to provide nearly double the total amount of electricity currently generated in the U.S., One of the most expensive components of a wind turbine is the gearbox; they also tend to be the part that’s most vulnerable to failure., Researchers at LBNL have been awarded new grants to develop fiber optics for two novel uses: monitoring offshore wind operations and underground natural gas storage., Wrapping fiber optic cables around the entire gearbox could help identify problems with the gearbox at an early stage which would trigger emergency management.   

    From Lawrence Berkeley National Lab: “Using Fiber Optics to Advance Safe and Renewable Energy” 

    From Lawrence Berkeley National Lab

    March 30, 2020
    Julie Chao
    JHChao@lbl.gov
    (510) 486-6491

    Berkeley Lab to develop innovative technologies to make offshore wind and natural gas storage more reliable.

    1
    Berkeley Lab is working to address barriers to more widespread deployment of offshore wind in California, where floating wind turbines could be a viable option. (Credit: SarahGower/iStock)

    Fiber optic cables, it turns out, can be incredibly useful scientific sensors. Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) have studied them for use in carbon sequestration, groundwater mapping, earthquake detection, and monitoring of Arctic permafrost thaw. Now they have been awarded new grants to develop fiber optics for two novel uses: monitoring offshore wind operations and underground natural gas storage.

    “A fiber cable has a glass core that allows you to send an optical signal down at the speed of light; when there is any vibration, strains, or stresses or changes in temperature of the material that is being monitored, that information will be carried in the light signal that is scattered back,” said Berkeley Lab scientist Yuxin Wu, who is leading both projects.

    The California Energy Commission has awarded Berkeley Lab $2 million for the offshore wind project and $1.5 million for the natural gas project. Both projects are in collaboration with UC Berkeley, and for the natural gas project, Berkeley Lab will also collaborate with PG&E, Schlumberger, and C-FER Technologies (a Canadian company), to carry out the tests.

    From gearbox failure to humpback whale movements

    Europe is at the forefront of offshore wind development. Other parts of the world are only in the early stages of commercialization, but it is growing quickly, including in the U.S., where the Department of Energy (DOE) has been supporting development of the technology. Offshore wind resources in the U.S. are abundant and have the potential to provide nearly double the total amount of electricity currently generated in the U.S., according to a 2016 DOE report.

    One of the advantages of offshore wind for the U.S. is that the resource is close to dense coastal populations. Therefore, energy transmission is a lesser challenge compared to other renewable energy sources such as onshore wind and solar farms, which are typically located farther away from population centers due to the availability and cost of real estate.

    Off the California coast, the ocean floor drops off steeply, making floating wind turbines – which are tethered to the ocean floor by mooring chains, unlike conventional “fixed bottom” offshore wind turbines – the only viable option. But this technology faces several obstacles, including how to do maintenance and operations on remote installations in the ocean economically and how to monitor if hazards such as earthquakes or extreme weather conditions disrupt operations.

    This is where the fiber optic cables come in.

    “One of the most expensive components of a wind turbine is the gearbox; they also tend to be the part that’s most vulnerable to failure,” said Wu, who is also head of Berkeley Lab’s Geophysics Department. “Often before they fail they produce abnormal vibrations or excessive heat due to increased or irregular friction. We intend to use fiber optic cables to monitor the vibrational, strain, and temperature signal of the gearbox, in order to pinpoint where problems are happening.”

    Wrapping fiber optic cables around the entire gearbox can provide a 3D map of changes with resolution at the millimeter scale. “It could help identify problems with the gearbox at an early stage, which would trigger emergency management, before a catastrophic failure causing loss of the whole turbine,” Wu said.

    What’s more, Wu said the project intends to explore how the fiber optic cables can be used to detect marine mammal activity. The sensitivity of the fiber signal could allow for differentiation between, say, crashing waves and a pod of whales swimming by.

    “Environmentally sustainable development of offshore wind is critical,” he said. “With a large offshore wind farm, there would be many of these mooring lines securing the turbine structures to the ocean floor. If a humpback whale swims by, what are the impacts of these mooring lines on their activities? Will the whales generate unique vibrational signals that can be picked up by the fiber optic sensors? If we can track the signals of a whale swimming by, it will allow us to evaluate whether and how the offshore wind turbine impacts marine mammals.”

    Wu added that he is looking to learn more about whales and other marine mammals from marine biologists and also seeking a partner to collaborate with to test the sensors in the ocean.

    Making underground gas reservoirs safer

    Similarly, Wu and his research partners hope to use fiber optic cables to monitor the boreholes of underground natural gas storage reservoirs. The borehole is used to inject and withdraw gas from vast underground storage reservoirs. Like any pipe, these boreholes degrade and corrode over time. The massive gas leak at Aliso Canyon in 2016, in which thousands of families had to evacuate their homes, was concluded to be caused by corrosion damage of the borehole.

    Thus, borehole integrity is of paramount importance to safe storage of natural gas in the subsurface. It is currently monitored mostly using tools that are intrusive, expensive, and incapable of providing frequent, real-time data. “It is difficult to predict borehole degradation trajectory with the sparse data generated by traditional methods. Having higher frequency datasets covering the entire borehole is key to provide an early warning of potential borehole failures,” Wu said.

    In the new CEC-funded project, Berkeley Lab will work with UC Berkeley, PG&E, Schlumberger, and C-FER to test a novel suite of technologies for autonomous real-time monitoring using two methods, one based on distributed strain, vibration, and temperature sensing in fiber optic cables and the other using electromagnetic wave reflectometry.

    EM-TDR (or electromagnetic time domain reflectometry) is similar to the fiber optic technology except that it uses longer wavelength electromagnetic waves instead of visible light (also an electromagnetic wave but at much short wavelength) as signals. “EM-TDR sends electromagnetic waves into an electronically conductive material, and when there is a change due to damage, such as corrosion, you get an EM signal back which can help you identify corrosion or other degradations,” Wu said.

    And because the borehole is made of steel, which is electrically conductive, no downhole equipment will need to be installed. Thus, EM-TDR is very easy to deploy and can be used under many circumstances that prevent the use of other types of sensors. On the other hand, EM-TDR is still an early-stage technology; this new project will allow further testing and development.

    For both the offshore wind and natural gas projects, the scientific challenge, Wu said, is optimizing the technology design and sensitivity and developing real-time edge computing technologies. “In addition to using commercial systems, our team is developing new fiber interrogators that will allow us to not only get to the original raw data but also play with the physics to better design a system that can give us the most sensitive signal we want,” he said. “In addition, we will be developing machine learning-based edge computing methods to turn raw data into actionable intelligence quickly. This is key for real-time monitoring.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 11:03 am on March 20, 2020 Permalink | Reply
    Tags: 2020 LHC Olympics, A team of cosmologists at Lawrence Berkeley National Laboratory (Berkeley Lab) developed a code that best identified a mock signal hidden in simulated particle-collision data., ATLAS detector at CERN’s Large Hadron Collider, LBNL   

    From Lawrence Berkeley National Lab: “Berkeley Lab Cosmologists Are Top Contenders in Machine Learning Challenge” 

    From Lawrence Berkeley National Lab

    March 20, 2020
    Glenn Roberts Jr.
    geroberts@lbl.gov
    (510) 520-0843

    Teams searched for hidden signal in simulated particle collider data.

    1
    The 2020 LHC Olympics challenged teams to develop a machine learning code to find a hidden signal in particle-collision data. This image shows particle-collision data captured by the ATLAS detector at CERN’s Large Hadron Collider. (Credit: CERN)

    In searching for new particles, physicists can lean on theoretical predictions that suggest some good places to look and some good ways to find them: It’s like being handed a rough sketch of a needle hidden in a haystack.

    But blind searches are a lot more complicated, like hunting in a haystack without knowing what you are looking for.

    To find what conventional computer algorithms and scientists may overlook in the huge volume of data collected in particle collider experiments, the particle physics community is turning to machine learning, an application of artificial intelligence that can teach itself to improve its searching skills as it sifts through a haystack of data.

    In a machine learning challenge dubbed the 2020 Large Hadron Collider (LHC) Olympics, a team of cosmologists from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) developed a code that best identified a mock signal hidden in simulated particle-collision data.

    Cosmologists? That’s right.

    It was totally unexpected for us to perform so well,” said George Stein, a Berkeley Lab and UC Berkeley postdoctoral researcher who participated in the challenge with Uros Seljak, a Berkeley Lab cosmologist, UC Berkeley professor, and co-director of the Berkeley Center for Cosmological Physics, of which Stein is a member.

    Ten teams, composed mostly of particle physicists, competed in the competition, which ran from Nov. 19, 2019, to Jan. 12, 2020.

    Stein led the adaptation of a code that two other student researchers had developed under Seljak’s direction. The competition was launched by the organizers of the Machine Learning for Jets 2020 (ML4Jets2020) conference. Jets are narrow cones of particles produced in particle-collision experiments that particle physicists can trace back to measure the properties of their particle sources.

    The competition results were announced during the conference, which was held at New York University Jan. 15-17.

    Ben Nachman, a Berkeley Lab postdoctoral researcher who is part of a group that works on ATLAS – a large detector at CERN’s LHC – served as one of the event and contest organizers. David Shih, a physics and astronomy professor at Rutgers University now on a sabbatical at Berkeley Lab, and Gregor Kasieczka, a professor at the University of Hamburg in Germany, were co-organizers.

    While some computing competitions allow participants to submit and test their codes multiple times to gauge whether they are getting closer to the correct results, the 2020 LHC Olympics competition gave teams just one shot to submit a solution.

    “The cool thing is that we didn’t use an off-the-shelf tool,” Seljak said. “We used a tool that we had developed for our research.”

    He noted, “In my group we had been working on unsupervised machine learning. The idea is that you want to describe data where the data have no labels.”

    The tool that the team used is called sliced iterative optimal transport. “It’s a form of deep learning, but a form where we do not optimize everything at once,” Seljak said. “Instead, we do it iteratively,” in stages.

    The code is so efficient that it can run on a simple desktop or laptop computer. It was developed for a statistical approach known as Bayesian evidence.

    Seljak said, “Suppose you are looking at anomalies in a planet’s transit time,” the time it takes for the planet to pass in front of a larger object from your viewpoint – like watching from Earth as Mercury moves in front of the sun.

    “One solution requires that there be an extra planet,” he said, “and the other solution requires an extra moon, and they are both a good fit to the data, but they have very different parameters. How do I compare these two solutions?”

    The Bayesian approach is to compute the evidence for both solutions and see which solution has a higher probability of being true.

    “This kind of example comes up all of the time,” Seljak said, and his team’s code is designed to speed up the complex calculations required by conventional methods. “We were trying to improve upon something unrelated to particle physics, and we realized this could be used as a general machine learning tool.”

    He added, “Our solution is particularly useful for so-called anomaly detection: looking for very tiny signals in data that are somehow different than its other data.”

    In the 2020 LHC Olympics competition, participants first received a sample set of data that called out particle signal data from some background data – both the needle and the haystack – that allowed participants to test their codes.

    Then they received the actual “black box” contest data: just the haystack. They were tasked to find a different and entirely unknown kind of particle signal hidden in the background data, and to specifically describe the signal events that their methods turned up.

    Competition co-organizers Shih and Nachman noted that they had personally been working on an anomaly-detection method that uses a very similar approach (called “conditional density estimation”) to the technique developed by Seljak and Stein that was entered in the competition.

    Seljak and Stein consulted with a number of particle physicists at the lab, including Nachman, Shih, and graduate student Patrick McCormack. They discussed, among other topics, how the high-energy physics community typically analyzes datasets like those used in the competition, but for the actual “black box” challenge Seljak and Stein were on their own.

    As the competition was drawing toward a close, Stein said, “We thought we found something about a week before the deadline.”

    Stein and Seljak submitted their results a few days before the conference, “but as we are not particle physicists, we were not planning to participate at the conference,” Seljak said.

    Then, Stein received an email from the conference organizers, who asked him to fly out and present a talk on the team’s solution later that week. The organizers didn’t share the results of the competition until all of the speakers had presented their results.

    My talk was originally first, and then shortly before the start of the session they moved me to last. I didn’t know if that was a good thing,” Stein said.

    The code that the Berkeley Lab team entered picked up about 1,000 events, with an error margin of plus or minus 200, and the correct response was 843 events. Their code was the clear winner in that category.

    Several teams were close in estimating the energy level, or “resonance mass,” of the signal, and the Berkeley Lab team was closest in its estimate of the resonance mass for a secondary signal stemming from the main signal.

    At the conference, Stein noted, “There was a huge interest in the overall approach we took. It made waves.”

    Oz Amram, another competitor in the contest, quipped in a Twitter post, “The result of the LHC Olympics … is that cosmologists are better at our job than we are.” But contest organizers did not formally announce a winner.

    Nachman, one of the event organizers, said, “Even though George and Uros clearly outperformed the other competitors, in the end it is likely that no one algorithm will cover every possibility – so we will need a diverse set of approaches to achieve broad sensitivity.”

    He added, “Particle physics has entered an interesting time where every prediction for new particles we have tested at the Large Hadron Collider has so far turned out to be not realized in nature – except the Standard Model of particle physics. While it is essential to continue the program of model-driven searches, we also have to develop a parallel program to be model-agnostic. That is the motivation for this challenge.”

    Seljak said that his team is planning to publish a paper that details its machine learning code.

    “We are definitely planning to apply this to many astrophysics problems,” he said. “We will look for interesting applications – anything with glitches or transients, anything anomalous. We will work to speed up the code and make it more powerful. These kinds of approaches can really help.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 11:30 am on March 19, 2020 Permalink | Reply
    Tags: "Nature-Inspired Green Energy Technology Clears Major Development Hurdle", , Building a solar fuel farm out of many individual tiles could proceed quickly., LBNL, The Molecular Foundry at LBNL   

    From Lawrence Berkeley National Lab: “Nature-Inspired Green Energy Technology Clears Major Development Hurdle” 

    From Lawrence Berkeley National Lab

    March 19, 2020
    Aliyah Kovner
    akovner@lbl.gov

    A new design has put the long-sought idea of artificial photosynthesis within reach.

    1
    Heinz Frei (right) talks about the potential renewable energy technology he developed with Georgios Katsoukis (center) and Won Jun Jo (left), who is holding a small piece of the team’s solar fuel tile material. (Credit: Marilyn Sargent/Berkeley Lab.)

    Scientist Heinz Frei has spent decades working toward building an artificial version of one of nature’s most elegant and effective machines: the leaf.

    Frei, and many other researchers around the world, seek to use photosynthesis – the sunlight-driven chemical reaction that green plants and algae use to convert carbon dioxide (CO2) into cellular fuel – to generate the kinds of fuel that can power our homes and vehicles. If the necessary technology could be refined past theoretical models and lab-scale prototypes, this moonshot idea, known as artificial photosynthesis, has the potential to generate large sources of completely renewable energy using the surplus CO2 in our atmosphere.

    With their latest advance, Frei and his team at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) are now closing in on this goal. The scientists have developed an artificial photosynthesis system, made of nanosized tubes, that appears capable of performing all the key steps of the fuel-generating reaction.

    Their latest paper, published in Advanced Functional Materials, demonstrates that their design allows for the rapid flow of protons from the interior space of the tube, where they are generated from splitting water molecules, to the outside, where they combine with CO2 and electrons to form the fuel. That fuel is currently carbon monoxide, but the team is working toward making methanol. Fast proton flow, which is essential for efficiently harnessing sunlight energy to form a fuel, has been a thorn in the side of past artificial photosynthesis systems.

    3
    A sample of the solar fuel tile material, made by atomic layer deposition at Berkeley Lab’s Molecular Foundry. (Credit: Marilyn Sargent/Berkeley Lab.)

    Now that the team has showcased how the tubes can perform all the photosynthetic tasks individually, they are ready to begin testing the complete system. The individual unit of the system will be small square “solar fuel tiles” (several inches on a side) containing billions of the nanoscale tubes sandwiched between a floor and ceiling of thin, slightly flexible silicate, with the tube openings piercing through these covers. Frei is hopeful that his group’s tiles could be the first to address the major hurdles still facing this type of technology.

    “There are two challenges that have not yet been met,” said Frei, who is a senior scientist in Berkeley Lab’s Biosciences Area. “One of them is scalability. If we want to keep fossil fuels in the ground, we need to be able to make energy in terawatts – an enormous amount of fuel. And, you need to make a liquid hydrocarbon fuel so that we can actually use it with the trillions of dollars’ worth of existing infrastructure and technology.”

    He noted that once a model meeting these requirements is made, building a solar fuel farm out of many individual tiles could proceed quickly. “We, as basic scientists, need to deliver a tile that works, with all questions about its performance settled. And engineers in industry know how to connect these tiles. When we’ve figured out square inches, they’ll be able to make square miles.”

    How it works

    4
    A microscopy image (top figure) of the nanotubes, generated in a sheet and (bottom image) a schematic of the layers that each tiny tube is composed of. Embedded in the silica layer are “molecular wires” made of short hydrocarbon chains that attach to the cobalt oxide on the inside and connect to the silica-titanium dioxide boundary on the opposite side. These wires conduct charges, which are generated by light absorbing molecules at that boundary, across the membrane to the cobalt oxide, enabling water oxidation. (Credit: Heinz Frei and Zosia Rostomian/Berkeley Lab.)

    Each tiny (about 0.5 micrometer wide), hollow tube inside the tile is made of three layers: an inner layer of cobalt oxide, a middle layer of silica, and an outer layer of titanium dioxide. In the inner layer of the tube, energy from sunlight delivered to the cobalt oxide splits water (in the form of moist air that flows through the inside of each tube), producing free protons and oxygen.

    “These protons easily flow through to the outer layer, where they combine with carbon dioxide to form carbon monoxide now – and methanol in a future step – in a process enabled by a catalyst supported by the titanium dioxide layer,” said Won Jun Jo, a postdoctoral fellow and first author of the paper. “The fuel gathers in the space between tubes, and can be easily drained out for collection.”

    Importantly, the middle layer of the tube wall keeps the oxygen produced from water oxidation in the interior of the tube, and blocks the carbon dioxide and the evolving fuel molecules on the outside from permeating into the interior, thereby separating the two very incompatible chemical reaction zones.

    This design mimics actual living photosynthetic cells, which separate oxidation and reduction reactions with organic membrane compartments inside the chloroplast. Similarly in line with nature’s original blueprint, the team’s membrane tubes allow the photosynthetic reaction to occur over a very short distance, minimizing the energy loss that occurs as ions travel and preventing unintended chemical reactions that would also lower the system’s efficiency.

    “This work is part of Berkeley Lab’s commitment to contribute solutions to the urgent energy challenges posed by climate change,” said Frei. “The interdisciplinary nature of the task requires the breadth of expertise and major facilities unique to Berkeley Lab. In particular, the nanofabrication and imaging capabilities of the Molecular Foundry are essential for synthesizing and characterizing the ultrathin layers and making square-inch-sized arrays of hollow nanotubes.”

    Funding to support this work was provided by the Energy & Biosciences Institute through the EBI‐Shell program. Portions of this work were performed at the Berkeley Lab’s Molecular Foundry, a DOE Office of Science user facility.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    LBNL campus

    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

    A U.S. Department of Energy National Laboratory Operated by the University of California.

    University of California Seal

     
  • richardmitnick 2:26 pm on March 18, 2020 Permalink | Reply
    Tags: "Three national laboratories achieve record magnetic field for accelerator focusing magnet", , , LBNL, Magnets for the HL-LHC., The ingredient that sets these U.S.-produced magnets apart is niobium-tin.   

    From Fermi National Accelerator Lab: “Three national laboratories achieve record magnetic field for accelerator focusing magnet” 

    FNAL Art Image
    FNAL Art Image by Angela Gonzales

    From Fermi National Accelerator Lab , an enduring source of strength for the US contribution to scientific research world wide.

    March 18, 2020

    Media contacts

    Fermilab Office of Communication, media@fnal.gov, 630-840-3351
    Karen McNulty Walsh, Brookhaven National Laboratory, kmcnulty@bnl.gov, 631-344-8350, 917-699-0501
    Laurel Kellner, Lawrence Berkeley National Laboratory, lkellner@lbl.gov, 510-590-8034

    In a multiyear effort involving three national laboratories from across the United States, researchers have successfully built and tested a powerful new magnet based on an advanced superconducting material. The eight-ton device — about as long as a semi-truck trailer — set a record for the highest field strength ever recorded for an accelerator focusing magnet and raises the standard for magnets operating in high-energy particle colliders.

    The Department of Energy’s Fermilab, Brookhaven National Laboratory and Lawrence Berkeley National Laboratory designed, built and tested the new magnet, one of 16 they will provide for operation in the High-Luminosity Large Hadron Collider at CERN laboratory in Europe.

    The 16 magnets, along with another eight produced by CERN, serve as “optics” for charged particles: They will focus beams of protons into a tiny, infinitesimal spot as they approach collision inside two different particle detectors.

    The ingredient that sets these U.S.-produced magnets apart is niobium-tin – a superconducting material that produces strong magnetic fields. These will be the first niobium-tin quadrupole magnets ever to operate in a particle accelerator.

    Like the current Large Hadron Collider, its high-luminosity successor will smash together beams of protons cruising around the 17-mile ring at close to the speed of light. The HL-LHC will pack an additional punch: It will provide 10 times the collisions that are possible at the current LHC. With more collisions come more opportunities to discover new physics.

    And the machine’s new focusing magnets will help it achieve that leap in delivered luminosity.

    “We’ve demonstrated that this first quadrupole magnet behaves successfully and according to design, based on the multiyear development effort made possible by DOE investments in this new technology,” said Fermilab scientist Giorgio Apollinari, head of the U.S. Accelerator Upgrade Project, which leads the U.S.-based focusing-magnet project.

    “It’s a very cutting-edge magnet, really on the edge of magnet technology,” said Brookhaven National Laboratory scientist Kathleen Amm, the Brookhaven representative for the Accelerator Upgrade Project.

    What makes it successful is its impressive ability to focus.

    2
    This new magnet reached the highest field strength ever recorded for an accelerator focusing magnet. Designed and built by Fermilab, Brookhaven National Laboratory and Lawrence Berkeley National Laboratory, it will be the first niobium-tin quadrupole magnet ever to operate in a particle accelerator — in this case, the future High-Luminosity Large Hadron Collider at CERN. Photo: Dan Cheng, Lawrence Berkeley National Laboratory

    Focus, magnets, focus

    In circular colliders, two beams of particles race around the ring in opposite directions. An instant before they reach the collision point, each beam passes through a series of magnets that focus the particle beams into a tiny, infinitesimal spot, much the way lenses focus light rays to a point. Now packed as tightly with particles as the magnets can get them — smash! — the beams collide.

    The scientific fruitfulness of that smash depends on how dense the beam is. The more particles that are crowded into the collision point, the greater the chance of particle collisions.

    You get those tightly packed beams by sharpening the magnet’s focus. One way to do that is to widen the lens. Consider light:

    “If you try to focus the light from the sun using a magnifying glass at a small point, you want to have a more ‘powerful’ magnifying glass,” said Ian Pong, Berkeley Lab scientist and one of the control account managers.

    A larger magnifying glass focuses more of the sun’s rays than a smaller one. However, the light rays at the outer rim of the lens have to be bent more sharply in order to approach the same focal point.

    Or consider a group of archers shooting arrows at an apple: More arrows will stick if the archers shoot from above, below and either side of the apple than if they are stationed at one post, firing from the same position.

    The analog of the magnifying glass size and the archer array is the magnet’s aperture — the opening of the passageway the beam takes as it barrels through the magnet’s interior. If the particle beam is allowed to start wide before being focused, more particles will arrive at the intended focal point — the center of the particle detector.

    The U.S. team widened the LHC focusing magnet’s aperture to 150 millimeters, more than double the current aperture of 70 millimeters.

    But of course, a wider aperture isn’t enough. There is still the matter of actually focusing the beam, which means forcing a dramatic change in the beam’s size, from wide to narrow, by the time the beam reaches the collision point. And that requires an exceptionally strong magnet.

    “The magnet has to squeeze the beam more powerfully than the LHC’s present magnets in order to create the luminosity needed for the HL-LHC,” Apollinari said.

    To meet the demand, scientists designed and constructed a muscular focusing magnet, calculating that, at the required aperture, it would have to generate a field exceeding 11.4 teslas. This is up from the current 7.5-tesla field generated by the niobium-titanium-based LHC quadrupole magnets. (For accelerator experts: The HL-LHC integrated luminosity goal is 3,000 inverse femtobarns.)

    In January, the three-lab team’s first HL-LHC focusing magnet delivered above the goal performance, achieving an 11.5-tesla field and running continuously at this strength for five straight hours, just as it would operate when the High-Luminosity LHC starts up in 2027.

    “These magnets are the currently highest-field focusing magnets in accelerators as they exist today,” Amm said. “We’re really pushing to higher fields, which allows us to get to higher luminosities.”

    The new focusing magnet was a triumph, thanks to niobium-tin.

    Magnet makers: Three U.S. labs are building powerful magnets for the world’s largest powerful collider from Berkeley Lab on Vimeo.

    Niobium-tin for the win

    The focusing magnets in the current LHC are made with niobium-titanium, whose intrinsic performance limit is generally recognized to have been reached at 8 to 9 teslas in accelerator applications.

    The HL-LHC will need magnets with around 12 teslas, about 250,000 times stronger than the Earth’s magnetic field at its surface.

    “So what do you do? You need to go to a different conductor,” Apollinari said.

    Accelerator magnet experts have been experimenting with niobium-tin for decades. Electrical current coursing through a niobium-tin superconductor can generate magnetic fields of 12 teslas and higher — but only if the niobium and tin, once mixed and heat treated to become superconductive, can stay intact.

    “Once they’re reacted, it becomes a beautiful superconductor that can carry a lot of current, but then it also becomes brittle,” Apollinari said.

    Famously brittle.

    “If you bend it too much, even a little bit, once it’s a reacted material, it sounds like corn flakes,” Amm said. “You actually hear it break.”

    Over the years, scientists and engineers have figured out how to produce niobium-tin superconductor in a form that is useful. Guaranteeing that it would hold up as the star of an HL-LHC focusing magnet was another challenge altogether.

    Berkeley, Brookhaven and Fermilab experts made it happen. Their assembly process is a delicate, involved operation balancing niobium-tin’s fragility against the massive changes in temperature and pressure it undergoes as it becomes the primary player in a future collider magnet.

    The process starts with wires containing niobium filaments surrounding a tin core, provided by an outside manufacturer. The wires are then fabricated into cables at Berkeley in just the right way. The teams at Brookhaven and Fermilab then wind these cables into coils, careful to avoid deforming them excessively. They heat the coils in a furnace in three temperature stages, a treatment that takes more than a week. During heat treatment the tin reacts with the filaments to form the brittle niobium-tin.

    Having been reacted in the furnace, the niobium-tin is now at its most fragile, so it is handled with care as the team cures it, embedding it in a resin to become a solid, strong coil.

    That coil is now ready to serve as one of the focusing magnet’s four poles. The process takes several months for each pole before the full magnet can be assembled.

    “Because these coils are very powerful when they are energized, there is a lot of force trying to push the magnet apart,” Pong said. “Even if the magnet is not deforming, at the conductor level there will be a strain, to which niobium-tin’s performance is very sensitive. The management of the stress is very, very important for these high-field magnets.”

    Heat treating the magnet coils — one of the intermediate steps in the magnet’s assembly — is also a subtle science. Each of the four coils of an HL-LHC focusing magnet weighs about one ton and has to be heat-treated evenly — inside and out.

    “You have to control the temperature well. Otherwise the reaction will not give us the best performance,” Pong said. “It’s a bit like cooking. It’s not just to achieve the temperature in one part of the coil but in the entire coil, end to end, top to bottom, the whole thing.”

    And the four coils have to be aligned precisely with one another.

    “You need very high field precision, so we have to have very high precision in how they align these to get good magnetic-field uniformity, a good quadrupole field,” Amm said.

    The fine engineering that goes into the U.S. HL-LHC magnets has sharpened over decades, with a payoff that is energizing the particle accelerator community.

    “This will be the first use of niobium-tin in accelerator focusing magnets, so it will be pretty exciting to see such a complex and sophisticated technology get implemented into a real machine,” Amm said.

    “We were always carrying the weight of responsibility, the hope in the last 10, 20 years — and if you want to go further, 30, 40 years — focusing on these magnets, on conductor development, all the work,” Pong said. “Finally, we are coming to it, and we really want to make sure it is a lasting success.”

    5
    The magnet gets ready for a test at Brookhaven National Laboratory. Photo: Brookhaven National Laboratory.

    The many moving parts of an accelerator collaboration

    Ensuring lasting success has as much to do with the operational choreography as it does with the exquisite engineering. Conducting logistics that span years and a continent requires painstaking coordination.

    “Planning and scheduling are very important, and they’re quite challenging,” Pong said. “For example, transportation communication: We have to make sure that things are well protected. Otherwise these expensive items can be damaged, so we have to foresee issues and prevent them. Delays also have an impact on the whole project, so we have to ensure components are shipped to destination in a timely schedule.”

    Amm, Apollinari and Pong acknowledge that the three-lab team have met the challenges capably, operating as a well-oiled machine.

    “The technologies developed at Fermilab, Brookhaven and Berkeley helped make the original LHC a success. And now again, these technologies out of the U.S. are really helping CERN be successful,” Amm said. “It’s a dream team, and it’s an honor to be a part of it.”

    The U.S.-based Accelerator Upgrade Project for the HL-LHC, of which the focusing-magnet project is one piece, kicked off in 2016, growing out of a 2003 predecessor R&D program that focused on similar accelerator technology projects.

    From now until about 2025, the U.S. labs will continue to build the large, hulking tubes, starting with fine strands of niobium and tin. They plan to begin delivering in 2022 the first of 16 magnets, plus four spares, to CERN. Installation will take place over the three years following.

    “People say that ‘touchdown’ is a very beautiful word to describe the landing of an airplane, because you have a huge metal object weighing hundreds of tons, descending from the sky, touching a concrete runway very gently,” Pong said. “These magnets are not too different from that. Our magnets are massive superconducting devices, focusing tiny invisible particle beams that are flying close to the speed of light through the bore. It’s quite magical.”

    The magic starts in 2027, when the High-Luminosity LHC comes online.

    “We are doing today the work that future young researchers will use in 10 or 20 years from now to push the frontier of human knowledge, just like it happened when I was a young researcher here at Fermilab, using the Tevatron,” Apollinari said. “It’s a generational passing of the baton. We need to make the machines for the future generations, and with this technology, obviously what we can enable for the future generation is a lot.”

    Learn more about the High-Luminosity LHC in Symmetry and in an 11-minute Fermilab YouTube video.

    Fermilab is America’s premier national laboratory for particle physics and accelerator research. A U.S. Department of Energy Office of Science laboratory, Fermilab is located near Chicago, Illinois, and operated under contract by the Fermi Research Alliance LLC, a joint partnership between the University of Chicago and the Universities Research Association, Inc. Visit Fermilab’s website at http://www.fnal.gov and follow us on Twitter at @Fermilab.

    This accelerator magnet work is supported by the Department of Energy Office of Science.

    See the full here.


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    Fermi National Accelerator Laboratory (Fermilab), located just outside Batavia, Illinois, near Chicago, is a US Department of Energy national laboratory specializing in high-energy particle physics. Fermilab is America’s premier laboratory for particle physics and accelerator research, funded by the U.S. Department of Energy. Thousands of scientists from universities and laboratories around the world
    collaborate at Fermilab on experiments at the frontiers of discovery.

     
  • richardmitnick 11:11 pm on February 5, 2020 Permalink | Reply
    Tags: "Breakthrough made on the next big step to building the world's most powerful particle accelerator", (MICE) Muon Ionization Cooling Experiment collaboration, , For the first time scientists have observed muon ionization cooling., LBNL, , This new muon accelerator will give us a better understanding of the fundamental constituents of matter.   

    From Science and Technology Facilities Council: “Breakthrough made on the next big step to building the world’s most powerful particle accelerator” 


    From Science and Technology Facilities Council

    5 February 2020

    For the first time scientists have observed muon ionization cooling – a major step in being able to create the world’s most powerful particle accelerator. This new muon accelerator will give us a better understanding of the fundamental constituents of matter.

    Since the 1930s, accelerators have been used to make ever more energetic proton, electron, and ion beams. These beams have been used in practically every scientific field, from colliding particles in the Large Hadron Collider to measuring the chemical structure of drugs, treating cancers and the manufacture of the ubiquitous silicon microchip.

    Now, the international Muon Ionization Cooling Experiment (MICE) collaboration, which includes many UK scientists, has made a major step forward in the quest to create an accelerator for an entirely different sort of particle, a muon. A muon accelerator could replace the Large Hadron Collider (LHC), providing at least a ten-fold increase in energy for the creation of new particles.

    Until now, the question has been whether you can channel enough muons into a small enough volume to be able to study physics in new, unexplored systems. This new research, published in Nature today, shows that it is possible. The results of the experiment, carried out using the MICE muon beam-line at the Science and Technology Facilities Council (STFC) ISIS Neutron and Muon Beam facility on the Harwell Campus in the UK, clearly show that ionization cooling works and can be used to channel muons into a tiny volume.

    1
    Credit: STFC

    2
    MICE target during development testing. Credit: STFC

    3
    The target used to generate the muons for the experiment. Credit: STFC

    4
    The Muon Ionization Cooling Experiment, pictured here at Rutherford Appleton Laboratory in the United Kingdom, has for the first time successfully cooled a beam of muons, essentially focusing a diffuse cloud of muon particles. The collaborators used powerful superconducting magnetic lenses and specially designed energy absorbers to achieve this milestone. Photo: Rutherford Appleton Laboratory/UK Science and Technology Facilities Council. Provided by FNAL.

    6
    In this photo of the MICE experiment, superconducting spectrometer solenoids (horizontal cylinders with yellow and black tape) flank the muon ionization cooling channel. A Berkeley Lab team designed, built, and delivered the spectrometer solenoids. Provided by LBNL(Credit: Steve Virostek/Berkeley Lab)

    “The enthusiasm, dedication, and hard work of the international collaboration and the outstanding support of laboratory personnel at STFC and from institutes across the world have made this game-changing breakthrough possible,” said Professor Ken Long from Imperial College London, spokesperson for the experiment.

    Dr Chris Rogers, based at ISIS and the collaboration’s Physics Co-ordinator, explained: “MICE has demonstrated a completely new way of squeezing a particle beam into a smaller volume. This technique is necessary for making a successful muon collider, which could outperform even the LHC.”

    Muons have many uses – they can be used to study the atomic structure of materials, they can be used as a catalyst for nuclear fusion and they can be used to see through really dense materials which X-rays can’t get through. The research team hopes that this technique can help produce good quality muon beams for these applications as well.

    Muons are produced by smashing a beam of protons into a target. The muons can then be separated off from the debris created at the target and directed through a series of magnetic lenses. Because of this rough-and-ready production mechanism, these muons form a diffuse cloud – so when it comes to colliding the muons, the chances of them hitting each other and producing interesting physical phenomena is really low.

    To make the cloud less diffuse, a process called beam cooling is used. This involves getting the muons closer together and moving in the same direction. Magnetic lenses can get the muons closer together, or get them moving in the same direction, but not both at the same time.

    A major obstacle to cooling a muon beam this is that muons only live for two millionths of a second, and previous methods developed to cool beams take hours to achieve an effect. In the 1970s a new method called ‘ionization cooling’ had been suggested, and developed into theoretically operable schemes in the in the 1990s. The hurdle of testing this idea in practice remained formidable.

    The MICE collaboration developed the completely new method to tackle this unique challenge, cooling the muons by putting them through specially-designed energy-absorbing materials such as lithium hydride, a compound of lithium metal and hydrogen, or liquid hydrogen cooled to around minus 250 degrees Celsius and encased by incredibly thin aluminium windows. This was done while the beam was very tightly focussed by powerful superconducting magnetic lenses. The measurement is so delicate that it requires measuring the beam particle-by-particle using particle physics techniques rather than the usual accelerator diagnostics.

    After cooling the beam, the muons can be accelerated by a normal particle accelerator in a precise direction, making it much more likely for the muons to collide. Alternatively, the cold muons can be slowed down so that their decay products can be studied.

    Professor Alain Blondel, spokesperson of MICE from 2001 to 2013, and Emeritus Professor at the University of Geneva, said: “We started MICE studies in 2000 with great enthusiasm and a strong team from all continents. It is a great pride to see the demonstration achieved, just at a time when it becomes evident to many new people that we must include muon machines in the future of particle physics.”

    “In this era of ever more-expensive particle accelerators, MICE points the way to a new generation of cost-effective muon colliders,” said Professor Dan Kaplan, Director of the IIT Center for Accelerator and Particle Physics in Chicago.

    Professor Paul Soler from the University of Glasgow and UK Principal Investigator said: “Ionization cooling is a game-changer for the future of high-energy muon accelerators, such as a muon collider, and we are extremely grateful to all the international funding agencies, including STFC in the UK, for supporting the experiment and to the staff at the ISIS neutron and muon source for hosting the facility that made this result possible.”

    Notes

    “Demonstration of cooling by the Muon Ionization Cooling Experiment” was published in Nature on 5 February.

    About ISIS Neutron and Muon Source

    ISIS Neutron and Muon Source is a world-leading centre for research in the physical and life sciences at STFC’s Rutherford Appleton Laboratory near Oxford in the United Kingdom. Our suite of neutron and muon instruments gives unique insights into the properties of materials on the atomic scale. The neutron and muon beams produced at ISIS are used in research areas ranging from clean energy and the environment to pharmaceuticals, nanotechnology and IT.

    See the full article here .

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    STFC Rutherford Appleton Laboratory at Harwell in Oxfordshire, UK


    STFC Hartree Centre

    Helping build a globally competitive, knowledge-based UK economy

    We are a world-leading multi-disciplinary science organisation, and our goal is to deliver economic, societal, scientific and international benefits to the UK and its people – and more broadly to the world. Our strength comes from our distinct but interrelated functions:

    Universities: we support university-based research, innovation and skills development in astronomy, particle physics, nuclear physics, and space science
    Scientific Facilities: we provide access to world-leading, large-scale facilities across a range of physical and life sciences, enabling research, innovation and skills training in these areas
    National Campuses: we work with partners to build National Science and Innovation Campuses based around our National Laboratories to promote academic and industrial collaboration and translation of our research to market through direct interaction with industry
    Inspiring and Involving: we help ensure a future pipeline of skilled and enthusiastic young people by using the excitement of our sciences to encourage wider take-up of STEM subjects in school and future life (science, technology, engineering and mathematics)

    We support an academic community of around 1,700 in particle physics, nuclear physics, and astronomy including space science, who work at more than 50 universities and research institutes in the UK, Europe, Japan and the United States, including a rolling cohort of more than 900 PhD students.

    STFC-funded universities produce physics postgraduates with outstanding high-end scientific, analytic and technical skills who on graduation enjoy almost full employment. Roughly half of our PhD students continue in research, sustaining national capability and creating the bedrock of the UK’s scientific excellence. The remainder – much valued for their numerical, problem solving and project management skills – choose equally important industrial, commercial or government careers.

    Our large-scale scientific facilities in the UK and Europe are used by more than 3,500 users each year, carrying out more than 2,000 experiments and generating around 900 publications. The facilities provide a range of research techniques using neutrons, muons, lasers and x-rays, and high performance computing and complex analysis of large data sets.

    They are used by scientists across a huge variety of science disciplines ranging from the physical and heritage sciences to medicine, biosciences, the environment, energy, and more. These facilities provide a massive productivity boost for UK science, as well as unique capabilities for UK industry.

    Our two Campuses are based around our Rutherford Appleton Laboratory at Harwell in Oxfordshire, and our Daresbury Laboratory in Cheshire – each of which offers a different cluster of technological expertise that underpins and ties together diverse research fields.

    Daresbury Laboratory at Sci-Tech Daresbury in the Liverpool City Region,

    The combination of access to world-class research facilities and scientists, office and laboratory space, business support, and an environment which encourages innovation has proven a compelling combination, attracting start-ups, SMEs and large blue chips such as IBM and Unilever.

    We think our science is awesome – and we know students, teachers and parents think so too. That’s why we run an extensive Public Engagement and science communication programme, ranging from loans to schools of Moon Rocks, funding support for academics to inspire more young people, embedding public engagement in our funded grant programme, and running a series of lectures, travelling exhibitions and visits to our sites across the year.

    Ninety per cent of physics undergraduates say that they were attracted to the course by our sciences, and applications for physics courses are up – despite an overall decline in university enrolment.

     
  • richardmitnick 1:38 pm on January 29, 2020 Permalink | Reply
    Tags: "Particle Physics Turns to Quantum Computing for Solutions to Tomorrow’s Big-Data Problems", , , , LBNL, , ,   

    From Lawrence Berkeley National Lab: “Particle Physics Turns to Quantum Computing for Solutions to Tomorrow’s Big-Data Problems” 

    From Lawrence Berkeley National Lab

    January 29, 2020
    Glenn Roberts Jr.
    geroberts@lbl.gov
    (510) 486-5582

    Berkeley Lab researchers testing several techniques, technologies to be ready for the incoming deluge of particle data.

    1
    Display of a simulated High-Luminosity Large Hadron Collider (HL-LHC) particle collision event in an upgraded ATLAS detector. The event has an average of 200 collisions per particle bunch crossing. (Credit: ATLAS Collaboration/CERN)

    Giant-scale physics experiments are increasingly reliant on big data and complex algorithms fed into powerful computers, and managing this multiplying mass of data presents its own unique challenges.

    To better prepare for this data deluge posed by next-generation upgrades and new experiments, physicists are turning to the fledgling field of quantum computing to find faster ways to analyze the incoming info.

    Giant-scale physics experiments are increasingly reliant on big data and complex algorithms fed into powerful computers, and managing this multiplying mass of data presents its own unique challenges.

    To better prepare for this data deluge posed by next-generation upgrades and new experiments, physicists are turning to the fledgling field of quantum computing to find faster ways to analyze the incoming info.

    Click on a name or photo in the series of articles listed below profile three student researchers who have participated in Berkeley Lab-led efforts to learn about research projects in quantum computing by early-career researchers at Berkeley Lab:

    In a conventional computer, memory takes the form of a large collection of bits, and each bit has only two values: a one or zero, akin to an on or off position. In a quantum computer, meanwhile, data is stored in quantum bits, or qubits. A qubit can represent a one, a zero, or a mixed state in which it is both a one and a zero at the same time.

    By tapping into this and other quantum properties, quantum computers hold the potential to handle larger datasets and quickly work through some problems that would trip up even the world’s fastest supercomputers. For other types of problems, though, conventional computers will continue to outperform quantum machines.

    The High Luminosity Large Hadron Collider (HL-LHC) Project, a planned upgrade of the world’s largest particle accelerator at the CERN laboratory in Europe, will come on line in 2026.

    LHC

    CERN map


    CERN LHC Maximilien Brice and Julien Marius Ordan


    CERN LHC particles

    THE FOUR MAJOR PROJECT COLLABORATIONS

    ATLAS

    CERN ATLAS Image Claudia Marcelloni CERN/ATLAS

    ALICE

    CERN/ALICE Detector


    CMS
    CERN CMS New

    LHCb
    CERN LHCb New II

    It will produce billions of particle events per second – five to seven times more data than its current maximum rate – and CERN is seeking new approaches to rapidly and accurately analyze this data.

    In these particle events, positively charged subatomic particles called protons collide, producing sprays of other particles, including quarks and gluons, from the energy of the collision. The interactions of particles can also cause other particles – like the Higgs boson – to pop into existence.

    Tracking the creation and precise paths (called “tracks”) of these particles as they travel through layers of a particle detector – while excluding the unwanted mess, or “noise” produced in these events – is key in analyzing the collision data.

    The data will be like a giant 3D connect-the-dots puzzle that contains many separate fragments, with little guidance on how to connect the dots.

    To address this next-gen problem, a group of student researchers and other scientists at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have been exploring a wide range of new solutions.

    One such approach is to develop and test a variety of algorithms tailored to different types of quantum-computing systems. Their aim: Explore whether these technologies and techniques hold promise for reconstructing these particle tracks better and faster than conventional computers can.

    Particle detectors work by detecting energy that is deposited in different layers of the detector materials. In the analysis of detector data, researchers work to reconstruct the trajectory of specific particles traveling through the detector array. Computer algorithms can aid this process through pattern recognition, and particles’ properties can be detailed by connecting the dots of individual “hits” collected by the detector and correctly identifying individual particle trajectories.

    2
    A new wheel-shaped muon detector is part of the ATLAS detector upgrade at CERN. This wheel-shaped detector measures more than 30 feet in diameter. (Credit: Julien Marius Ordan/CERN)

    Heather Gray, an experimental particle physicist at Berkeley Lab and a UC Berkeley physics professor, leads the Berkeley Lab-based R&D effort – Quantum Pattern Recognition for High-Energy Physics (HEP.QPR) – that seeks to identify quantum technologies to rapidly perform this pattern-recognition process in very-high-volume collision data. This R&D effort is funded as part of the DOE’s QuantISED (Quantum Information Science Enabled Discovery for High Energy Physics) portfolio.

    The HEP.QPR project is also part of a broader initiative to boost quantum information science research at Berkeley Lab and across U.S. national laboratories.

    Other members of the HEP.QPR group are: Wahid Bhimji, Paolo Calafiura, Wim Lavrijsen, and former postdoctoral researcher Illya Shapoval, who explored quantum algorithms for associative memory. Bhimji is a big data architect at Berkeley Lab’s National Energy Research Scientific Computing Center (NERSC). Calafiura is chief software architect of CERN’s ATLAS experiment and a member of Berkeley Lab’s Computational Research Division (CRD). And Lavrijsen is a CRD software engineer who is also involved in CERN’s ATLAS experiment.

    Members of the HEP.QPR project have collaborated with researchers at the University of Tokyo and from Canada on the development of quantum algorithms in high-energy physics, and jointly organized a Quantum Computing Mini-Workshop at Berkeley Lab in October 2019.

    Gray and Calafiura were also involved in a CERN-sponsored competition, launched in mid-2018, that challenged computer scientists to develop machine-learning-based techniques to accurately reconstruct particle tracks using a simulated set of HL-LHC data known as TrackML. Machine learning is a form of artificial intelligence in which algorithms can become more efficient and accurate through a gradual training process akin to human learning. Berkeley Lab’s quantum-computing effort in particle-track reconstruction also utilizes this TrackML set of simulated data.

    Berkeley Lab and UC Berkeley are playing important roles in the rapidly evolving field of quantum computing through their participation in several quantum-focused efforts, including The Quantum Information Edge, a research alliance announced in December 2019.

    The Quantum Information Edge is a nationwide alliance of national labs, universities, and industry advancing the frontiers of quantum computing systems to address scientific challenges and maintain U.S. leadership in next-generation information technology. It is led by the DOE’s Berkeley Lab and Sandia National Laboratories.

    The series of articles listed below profile three student researchers who have participated in Berkeley Lab-led efforts to apply quantum computing to the pattern-recognition problem in particle physics:

    4
    Lucy Linder, while working as a researcher at Berkeley Lab, developed her master’s thesis – supervised by Berkeley Lab staff scientist Paolo Calafiura – about the potential application of a quantum-computing technique called quantum annealing for finding particle tracks. She remotely accessed quantum-computing machines at D-Wave Systems Inc. in Canada and at Los Alamos National Laboratory in New Mexico.

    Linder’s approach was to first format the particle-track simulated data as something known as a QUBO (quadratic unconstrained binary optimization) problem that formulated the problem as an equation with binary values: either a one or a zero. This QUBO formatting also helped prepare the data for analysis by a quantum annealer, which uses qubits to help identify the best possible solution by applying a physics principle that describes how objects naturally seek the lowest-possible energy state.
    Read More

    5
    Eric Rohm, an undergraduate student working on a contract at Berkeley Lab as part of the DOE’s Science Undergraduate Laboratory Internship program, developed a quantum approximate optimization algorithm (QAOA) using quantum-computing resources at Rigetti Computing in Berkeley, California. He was supervised by Berkeley Lab physicist Heather Gray.

    This approach used a blend of conventional and quantum computing techniques to develop a custom algorithm. The algorithm, still in refinement, has been tested on the Rigetti Quantum Virtual Machine, a conventional computer that simulates a small quantum computer. The algorithm may eventually be tested on a Rigetti quantum processing unit that is equipped with actual qubits.
    Read More

    6
    Amitabh Yadav, a student research associate at Berkeley Lab since November who is supervised by Gray and Berkeley Lab software engineer Wim Lavrijsen, is working to apply a quantum version of a convention technique called Hough transform to identify and reconstruct particle tracks using IBM’s Quantum Experience, a form of quantum computing.

    The classical Hough transform technique can be used to detect specific features such as lines, curves, and circles in complex patterns, and the quantum Hough transform technique could potentially call out more complex shapes from exponentially larger datasets. Read More

    See the full article here .

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    LBNL Molecular Foundry

    Bringing Science Solutions to the World
    In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.

    Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.

    Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.

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  • richardmitnick 1:11 pm on January 13, 2020 Permalink | Reply
    Tags: "Influential electrons? Physicists uncover a quantum relationship", How electron energies vary from region to region in a particular quantum state, LBNL, , , , Quantum hybridization in the relationships between moving electrons, , Spectromicroscopy   

    From New York University, the Lawrence Berkeley National Laboratory, Rutgers University, and MIT via phys.org: “Influential electrons? Physicists uncover a quantum relationship” 

    From

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    phys.org

    A team of physicists has mapped how electron energies vary from region to region in a particular quantum state with unprecedented clarity. This understanding reveals an underlying mechanism by which electrons influence one another, termed quantum “hybridization,” that had been invisible in previous experiments.

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    Credit: CC0 Public Domain

    The findings, the work of scientists at New York University, the Lawrence Berkeley National Laboratory, Rutgers University, and MIT, are reported in the journal Nature Physics.

    “This sort of relationship is essential to understanding a quantum electron system—and the foundation of all movement—but had often been studied from a theoretical standpoint and not thought of as observable through experiments,” explains Andrew Wray, an assistant professor in NYU’s Department of Physics and one of the paper’s co-authors. “Remarkably, this work reveals a diversity of energetic environments inside the same material, allowing for comparisons that let us spot how electrons shift between states.”

    The scientists focused their work on bismuth selenide, or Bi2Se3, a material that has been under intense investigation for the last decade as the basis of advanced information and quantum computing technologies. Research in 2008 and 2009 identified bismuth selenide to host a rare “topological insulator” quantum state that changes the way electrons at its surface interact with and store information.

    Studies since then have confirmed a number of theoretically inspired ideas about topological insulator surface electrons. However, because these particles are on a material’s surface, they are exposed to environmental factors not present in the bulk of the material, causing them to manifest and move in different ways from region to region.

    The resulting knowledge gap, together with similar challenges for other material classes, has motivated scientists to develop techniques for measuring electrons with micron- or nanometer- scale spatial resolution, allowing researchers to examine electron interaction without external interference.

    The Nature Physics research is one of the first studies to use this new generation of experimental tools, termed “”—and the first spectromicroscopy investigation of Bi2Se3. This procedure can track how the motion of surface electrons differs from region to region within a material. Rather than focusing on average electron activity over a single large region on a sample surface, the scientists collected data from nearly 1,000 smaller regions.

    By broadening the terrain through this approach, they could observe signatures of quantum hybridization in the relationships between moving electrons, such as a repulsion between electronic states that come close to one another in energy. Measurements from this method illuminated the variation of electronic quasiparticles across the material surface.

    “Looking at how the electronic states vary in tandem with one another across the sample surface reveals conditional relationships between different kinds of electrons, and it’s really a new way of studying a material,” explains Erica Kotta, an NYU graduate student and first author on the paper. “The results provide new insight into the physics of topological insulators by providing the first direct measurement of quantum hybridization between electrons near the surface.”

    See the full article here .

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