From Argonne National Laboratory: “Understanding ghost particle interactions”

News from From Argonne National Laboratory

September 28, 2020
Joseph E. Harmon

Cross sections of neutrino-nucleus interactions versus energy. Improved agreement between experiment and model calculations clearly shown for case of nucleon pair rather than single nucleon. Inset shows neutrino interacting with nucleus and ejecting a lepton. Credit: Image by Argonne National Laboratory.

Scientists often refer to the neutrino as the ​“ghost particle.” Neutrinos were one of the most abundant particles at the origin of the universe and remain so today. Fusion reactions in the sun produce vast armies of them, which pour down on the Earth every day. Trillions pass through our bodies every second, then fly through the Earth as though it were not there.

“While first postulated almost a century ago and first detected 65 years ago, neutrinos remain shrouded in mystery because of their reluctance to interact with matter,” said Alessandro Lovato, a nuclear physicist at the U.S. Department of Energy’s (DOE) Argonne National Laboratory.

Lovato is a member of a research team from four national laboratories that has constructed a model to address one of the many mysteries about neutrinos — how they interact with atomic nuclei, complicated systems made of protons and neutrons (“nucleons”) bound together by the strong force. This knowledge is essential to unravel an even bigger mystery — why during their journey through space or matter neutrinos magically morph from one into another of three possible types or flavors.

To study these oscillations, two sets of experiments have been undertaken at DOE’s Fermi National Accelerator Laboratory (MiniBooNE and NOvA).

FNAL/MiniBooNE

FNAL NOvA Near Detector.

In these experiments, scientists generate an intense stream of neutrinos in a particle accelerator, then send them into particle detectors over a long period of time (MiniBooNE) or five hundred miles from the source (NOvA).

FNAL/NOvA experiment map.

Knowing the original distribution of neutrino flavors, the experimentalists then gather data related to the interactions of the neutrinos with the atomic nuclei in the detectors. From that information, they can calculate any changes in the neutrino flavors over time or distance. In the case of the MiniBooNE and NOvA detectors, the nuclei are from the isotope carbon-12, which has six protons and six neutrons.

“Our team came into the picture because these experiments require a very accurate model of the interactions of neutrinos with the detector nuclei over a large energy range,” said Noemi Rocco, a postdoc in Argonne’s Physics division and Fermilab. Given the elusiveness of neutrinos, achieving a comprehensive description of these reactions is a formidable challenge.

The team’s nuclear physics model of neutrino interactions with a single nucleon and a pair of them is the most accurate so far. ​“Ours is the first approach to model these interactions at such a microscopic level,” said Rocco. ​“Earlier approaches were not so fine grained.”

One of the team’s important findings, based on calculations carried out on the now-retired Mira supercomputer at the Argonne Leadership Computing Facility (ALCF), was that the nucleon pair interaction is crucial to model neutrino interactions with nuclei accurately. The ALCF is a DOE Office of Science User Facility.

MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility.

“The larger the nuclei in the detector, the greater the likelihood the neutrinos will interact with them,” said Lovato. ​“In the future, we plan to extend our model to data from bigger nuclei, namely, those of oxygen and argon, in support of experiments planned in Japan and the U.S.”

Rocco added that ​“For those calculations, we will rely on even more powerful ALCF computers, the existing Theta system and upcoming exascale machine, Aurora.”

ANL ALCF Theta Cray XC40 supercomputer.

Depiction of ANL ALCF Cray Intel SC18 Shasta Aurora exascale supercomputer.

Scientists hope that, eventually, a complete picture will emerge of flavor oscillations for both neutrinos and their antiparticles, called ​“antineutrinos.” That knowledge may shed light on why the universe is built from matter instead of antimatter — one of the fundamental questions about the universe.

The paper, titled ​“Ab Initio Study of (νℓ,ℓ−) and (¯νℓ,ℓ+) Inclusive Scattering in 12C: Confronting the MiniBooNE and T2K CCQE Data,” is published in Physical Review X. Besides Rocco and Lovato, authors include J. Carlson (Los Alamos National Laboratory), S. Gandolfi (Los Alamos National Laboratory), and R. Schiavilla (Old Dominion University/Jefferson Lab).

The present research is supported by the DOE Office of Science. The team received ALCF computing time through DOE’s Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program.

See the full article here .

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Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

About the Advanced Photon Source

The U. S. Department of Energy Office of Science’s Advanced Photon Source (APS) at Argonne National Laboratory is one of the world’s most productive X-ray light source facilities. The APS provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nation’s economic, technological, and physical well-being. Each year, more than 5,000 researchers use the APS to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility. APS scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the APS.

This research used resources of the Advanced Photon Source, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

From Argonne National Laboratory: “Department of Energy funds Q-NEXT at $115 million over the next five years, with an additional $93 million pledged by partner organizations”

News from From Argonne National Laboratory

Q-NEXT, a collaboration involving the world’s leading minds from the national laboratories, universities and the private sector, is one of five National Quantum Information Science (QIS) Research Centers awarded by the Department of Energy (DOE) in August 2020. It is funded by DOE at $115 million over the next five years, with $15 million in fiscal year 2020 dollars and funding in subsequent years contingent on congressional appropriations. Additional funding from partner organizations totals $93 million. Advances in QIS have the potential to revolutionize information technologies, including quantum computing, quantum communications and quantum sensing.

Led by Argonne National Laboratory, Q-NEXT includes nearly 100 researchers from three DOE national laboratories, ten universities and ten leading U.S. quantum technology companies. Member organizations are leaders in many areas of QIS, including quantum information theory, high-performance computation, quantum experimental science, basic discovery science, advanced computing and high energy physics.

Headquartered at Argonne just outside Chicago, Q-NEXT leverages and adds to a robust Chicago regional quantum ecosystem that includes Q-NEXT partners at the University of Chicago, University of Illinois at Urbana-Champaign, Northwestern University and University of Wisconsin-Madison. The significant participation of SLAC National Accelerator Laboratory, Stanford University and other West Coast institutions in Q-NEXT, with two national laboratories and leading research universities, ties this region’s leading quantum programs and innovative spirit along with our industrial partners to form a truly national center.

Leading the way in next generation quantum science and engineering

Q-NEXT brings together leaders in national laboratories, academia and the private sector to create an innovation ecosystem that enables the translation of discovery science into technologies that benefit U.S. prosperity and security. Led by the U.S. Department of Energy’s Argonne National Laboratory, Q-NEXT includes three national laboratories, ten universities and ten of the U.S.’s leading quantum technology companies. These partnerships bring together not only world-leading experts, but also facilities and infrastructure to advance the frontiers of quantum information science and engineering.

Q-NEXT focuses on how to reliably control, store, and transmit quantum information at distances that could be as small as a computer chip or as large as the distance between Chicago and San Francisco. Addressing this challenge requires developing novel quantum materials and integrating them into devices and systems, developing new classes of ultra-precise sensors, and overcoming losses that occur when quantum information is communicated over long distances. We will also develop simulation and characterization tools that we can apply to these quantum systems.

Mission

The Q-NEXT mission is to deliver quantum interconnects and establish a national foundry to provide pristine materials for new quantum devices. With these capabilities, the center will demonstrate secure communication links, networks of sensors, and simulation and network testbeds. To achieve its mission, Q-NEXT’s strategy is to pursue three foundational thrusts (quantum foundries, extreme-scale characterization, and quantum simulation and sensing) with three science and technology thrusts (materials and integration, quantum sensing, and quantum communications).

See the full article here .

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Please help promote STEM in your local schools.

Stem Education Coalition

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

About the Advanced Photon Source

The U. S. Department of Energy Office of Science’s Advanced Photon Source (APS) at Argonne National Laboratory is one of the world’s most productive X-ray light source facilities. The APS provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nation’s economic, technological, and physical well-being. Each year, more than 5,000 researchers use the APS to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility. APS scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the APS.

This research used resources of the Advanced Photon Source, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

From University of Chicago and Argonne National Laboratory: Women in STEM- “UChicago physicist leaves mark on X-ray sciences as leader, mentor” Linda Young

From University of Chicago

and

Argonne National Laboratory

Aug 10, 2020
Maggie Hudson

For decades, Prof. Linda Young has made an impact both as a researcher and a mentor. She is pictured here with (left to right) Argonne colleagues Anthony DiChiara, Maria Chan and Anirudha Sumant. Photo courtesy of Argonne National Laboratory.

Argonne’s Linda Young searches for new X-ray laser uses and ways to support junior scientists.

Like many of us, University of Chicago physicist Linda Young is working from home these days, though her home is more unique than most.

“We live in Enrico Fermi’s old house,” she said. “I always hope that I’ll breathe some inspiration from being in this house, but I’m not sure if I have.”

Whether through Fermi’s inspiration or her own scientific prowess, Young—a part-time professor in UChicago’s Department of Physics—has built an impressive research career studying the interactions of X-rays with matter. She leads the atomic, molecular and optical (AMO) physics group at Argonne National Laboratory, where she previously served as the head of the X-ray Science Division—overseeing experiments at one of the world’s top X-ray sources.

Developing X-ray lasers

X-ray interactions with matter have a long and storied history, beginning with the discovery of X-rays in 1895. Scientists harnessed this very high energy form of light to reveal unseen secrets of our world, allowing us to glimpse the bones beneath our skin and to decode the unique arrangement of atoms that make up different molecules.

Over the past century, scientists have continuously improved the strength of X-ray light sources and used them in new ways to understand the makeup of materials. Ten years ago, Young said, these experiments took a huge leap forward with the development of a new type of X-ray source: the X-ray free-electron laser [XFEL].

“Now, because we have X-ray free-electron lasers, new life has been injected into the topic of X-ray interactions with matter,” Young said. “We suddenly can have X-ray pulses that are of very short duration, very short wavelength, and very high intensity.”

At Argonne, Young plays an instrumental role in understanding how these X-ray lasers work and what they can be used for. “In our group, we work together to figure out how we can really utilize these super strong, coherent X-ray pulses to divine the secrets of matter,” she said.

Though Young has risen through the ranks to become an expert in X-ray physics, she began her career at Argonne with a background in optical laser spectroscopy. She integrated this knowledge into the AMO physics group’s studies of atomic structure; in 1994, as the youngest scientist in the group, Young was promoted to group leader.

Young’s tenure as group leader coincided with the opening of the Advanced Photon Source (APS) synchrotron at Argonne [below], a kilometer-long electron storage ring used as a source of bright X-ray beams. To utilize the convenience and capabilities of this world-class laboratory, the group shifted its focus to X-ray science. Young hired new team members with expertise in X-ray physics and led the design of two beamlines—X-ray laboratories within APS with unique instruments and capabilities.

The AMO physics group pushed the boundaries of the study of X-rays’ interactions with matter, using facilities at the APS as well as other X-ray sources. The group’s success in the field and interest in powerful X-ray techniques led to their involvement with the­­­ first X-ray free-electron laser (XFEL).

Young travels to international laboratories to do groundbreaking research with the world’s best scientists, but she notes that these experiences have more than just a scientific impact. “I think doing experiments at light sources around the world is very enriching,” she said. “You get to have insight into different international perspectives and make friends around the globe.”

X-ray scientists compete for funding and acclaim, but when they come together at international laboratories, they work as a team to tackle big problems. Their dream, Young explained, is to use XFELs to look at complex molecules in a new way. The ultra-strong, ultra-short pulses of X-ray light should allow them to take snapshots of the locations of all the atoms in a molecule as it moves around in a solution. Putting these snapshots together could create a 3D image of a huge, complicated molecule like a protein.

Mentoring the next generation

Young brings these ideas back to the UChicago, where she teaches a graduate course on X-ray physics and applications. She enjoys sharing her passion for these complex experiments with students who would not typically work with advanced X-ray techniques. As she interacts with students, she adapts her course in response to their feedback and encourages students to pursue their interests through the lens of X-ray sciences.

Prof. Linda Young (center) at SLAC National Accelerator Laboratory with (left to right) Christoph Bostedt, Steve Southworth, John Bozek, Steve Pratt and Yuelin Li. (Photo by Brad Plummer/SLAC.)

“I think it’s really invigorating to teach students because they’re so eager to learn, and you learn a lot of things from them,” said Young.

Her willingness to learn and adapt has served her well as a mentor at both Argonne and UChicago. Young has mentored a number of junior scientists at Argonne, helping them make decisions about their career path and even assisting with connections for future job placements.

At UChicago, she works to make the physics department supportive of all students and serves as chair of the department’s equity, diversity and inclusion committee. She coordinates seminars with speakers from underrepresented groups in the sciences and hosted the 2020 American Physical Society Conference for Undergraduate Women in Physics at UChicago.

Young notes that amidst the growing movement against systemic racism, she has realized that these previous activities to promote diversity in the department were not enough. The committee has reached out through student-led town hall meetings and seeking feedback on how they can better support minorities in physics. In the first meeting, students requested more opportunities for mentorship, and Young is excited to help them achieve their goals.

As more student feedback comes in, Young is listening and ready to work for lasting change in the physics department. “I think that this is a really important time for committees to step up and really do something concrete. I am looking forward to doing whatever I can in my own way.”

See the full article here .

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Please help promote STEM in your local schools.

Stem Education Coalition

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

About the Advanced Photon Source

The U. S. Department of Energy Office of Science’s Advanced Photon Source (APS) at Argonne National Laboratory is one of the world’s most productive X-ray light source facilities. The APS provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nation’s economic, technological, and physical well-being. Each year, more than 5,000 researchers use the APS to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility. APS scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the APS.

This research used resources of the Advanced Photon Source, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

An intellectual destination

One of the world’s premier academic and research institutions, the University of Chicago has driven new ways of thinking since our 1890 founding. Today, UChicago is an intellectual destination that draws inspired scholars to our Hyde Park and international campuses, keeping UChicago at the nexus of ideas that challenge and change the world.

The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Our commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world.

We empower individuals to challenge conventional thinking in pursuit of original ideas. Students in the College develop critical, analytic, and writing skills in our rigorous, interdisciplinary core curriculum. Through graduate programs, students test their ideas with UChicago scholars, and become the next generation of leaders in academia, industry, nonprofits, and government.

UChicago research has led to such breakthroughs as discovering the link between cancer and genetics, establishing revolutionary theories of economics, and developing tools to produce reliably excellent urban schooling. We generate new insights for the benefit of present and future generations with our national and affiliated laboratories: Argonne National Laboratory, Fermi National Accelerator Laboratory, and the Marine Biological Laboratory in Woods Hole, Massachusetts.

The University of Chicago is enriched by the city we call home. In partnership with our neighbors, we invest in Chicago’s mid-South Side across such areas as health, education, economic growth, and the arts. Together with our medical center, we are the largest private employer on the South Side.

In all we do, we are driven to dig deeper, push further, and ask bigger questions—and to leverage our knowledge to enrich all human life. Our diverse and creative students and alumni drive innovation, lead international conversations, and make masterpieces. Alumni and faculty, lecturers and postdocs go on to become Nobel laureates, CEOs, university presidents, attorneys general, literary giants, and astronauts.

From insideHPC brought forward by Fermi National Accelerator Lab: “DOE Unveils Blueprint for ‘Unhackable’ Quantum Internet: Central Roles for Argonne, Univ. of Chicago, Fermilab”

From insideHPC

brought forward by

FNAL Art Image by Angela Gonzales

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

July 23, 2020

Photons entangled in a pair of loops on a 52-mile quantum network testbed.
Credit: Argonne National Laboratory

At a press conference held today at the University of Chicago, the U.S. Department of Energy (DOE) unveiled a strategy [OSTI] for the development of a national quantum internet intended to bring “the United States to the forefront of the global quantum race and usher in a new era of communications.”

An outgrowth of the National Quantum Initiative Act of 2018, an initial goal of the strategy is build a prototype of a communications system using quantum mechanics over the next decade. DOE said its 17 national laboratories will serve as the backbone of the intended quantum internet, which will rely on the laws of quantum mechanics to control and transmit information more securely. “Currently in its initial stages of development, the quantum internet could become a secure communications network and have a profound impact on areas critical to science, industry, and national security,” DOE said in its announcement today.

The announcement follows a meeting in February in New York between members of the national labs, universities and industry to the essential research to be accomplished, describing the engineering and design barriers and setting near-term goals for the project. Steps toward building the quantum internet are underway in the Chicago area, which DOE said has become a hub for quantum research. Last February, scientists from Argonne National Laboratory in Lemont, Illinois, and the University of Chicago entangled photons across a 52-mile “quantum loop” in the Chicago suburbs, “successfully establishing one of the longest land-based quantum networks in the nation,” according to DOE.

That network will be connected to DOE’s Fermilab, specializing in particle physics, in Batavia, IL, establishing a three-node, 80-mile testbed.

“The combined intellectual and technological leadership of the University of Chicago, Argonne, and Fermilab has given Chicago a central role in the global competition to develop quantum information technologies,” said Robert J. Zimmer, president of the University of Chicago. “This work entails defining and building entirely new fields of study, and with them, new frontiers for technological applications that can improve the quality of life for many around the world and support the long-term competitiveness of our city, state, and nation.”

In today’s announcement, DOE stated that “quantum transmissions is….exceedingly difficult to eavesdrop on as information passes between locations. Scientists plan to use that trait to make virtually unhackable networks.” Early adopters could include banks and health providers, applications for national security and aircraft communications – even, eventually, mobile phone users.

DOE said scientists are also exploring how the quantum internet could support exchange of vast amounts of data and how networks of ultra-sensitive quantum sensors could allow engineers to better monitor and predict earthquakes—a longtime and elusive goal—or to search for deposits of oil, gas, or minerals.

The report released today includes such research objectives as building and integrating quantum networking devices, routing quantum information and correcting errors.

See the full article here .

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Founded on December 28, 2006, insideHPC is a blog that distills news and events in the world of HPC and presents them in bite-sized nuggets of helpfulness as a resource for supercomputing professionals. As one reader said, we’re sifting through all the news so you don’t have to!

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From Argonne National Laboratory: “Four years of calculations lead to new insights into muon anomaly”

News from From Argonne National Laboratory

May 5, 2020
Christina Nunez

Using Argonne’s supercomputer Mira, researchers have come up with newly precise calculations aimed at understanding a key disconnect between physics theory and experimental measurements.

ANL ALCF MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

Two decades ago, an experiment at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory pinpointed a mysterious mismatch between established particle physics theory and actual lab measurements. When researchers gauged the behavior of a subatomic particle called the muon, the results did not agree with theoretical calculations, posing a potential challenge to the Standard Model — our current understanding of how the universe works.

Ever since then, scientists around the world have been trying to verify this discrepancy and determine its significance. The answer could either uphold the Standard Model, which defines all of the known subatomic particles and how they interact, or introduce the possibility of an entirely undiscovered physics. A multi-institutional research team (including Brookhaven, Columbia University, and the universities of Connecticut, Nagoya and Regensburg, RIKEN) have used Argonne National Laboratory’s Mira supercomputer to help narrow down the possible explanations for the discrepancy, delivering a newly precise theoretical calculation that refines one piece of this very complex puzzle. The work, funded in part by the DOE’s Office of Science through its Office of High Energy Physics and Advanced Scientific Computing Research programs, has been published in the journal Physical Review Letters.

A muon is a heavier version of the electron and has the same electric charge. The measurement in question is of the muon’s magnetic moment, which defines how the particle wobbles when it interacts with an external magnetic field. The earlier Brookhaven experiment, known as Muon g-2 [since moved to FNAL], examined muons as they interacted with an electromagnet storage ring 50 feet in diameter. The experimental results diverged from the value predicted by theory by an extremely small amount measured in parts per million, but in the realm of the Standard Model, such a difference is big enough to be notable.

FNAL Muon g-2 studio

Standard Model of Particle Physics, Quantum Diaries

“If you account for uncertainties in both the calculations and the measurements, we can’t tell if this is a real discrepancy or just a statistical fluctuation,” said Thomas Blum, a physicist at the University of Connecticut who co-authored the paper. ​“So both experimentalists and theorists are trying to improve the sharpness of their results.”

As Taku Izubuchi, a physicist at Brookhaven Lab who is a co-author on the paper, noted, ​“Physicists have been trying to understand the anomalous magnetic moment of the muon by comparing precise theoretical calculations and accurate experiments since the 1940s. This sequence of work has led to many discoveries in particle physics and continues to expand the limits of our knowledge and capabilities in both theory and experiment.”

If the discrepancy between experimental results and theoretical predictions is indeed real, that would mean some other factor — perhaps some yet-to-be discovered particle — is causing the muon to behave differently than expected, and the Standard Model would need to be revised.

The team’s work centered on a notoriously difficult aspect of the anomaly involving the strong interaction, which is one of four basic forces in nature that govern how particles interact, along with weak, electromagnetic, and gravitational interactions. The biggest uncertainties in the muon calculations come from particles that interact through the strong force, known as hadronic contributions. These hadronic contributions are defined by a theory called quantum chromodynamics (QCD).

The researchers used a method called lattice QCD to analyze a type of hadronic contribution, light-by-light scattering. ​“To do the calculation, we simulate the quantum field in a small cubic box that contains the light-by-light scattering process we are interested in,” said Luchang Jin, a physicist at the University of Connecticut and paper co-author. ​“We can easily end up with millions of points in time and space in the simulation.”

That’s where Mira came in. The team used the supercomputer, housed at the Argonne Leadership Computing Facility (ALCF), to solve the complex mathematical equations of QCD, which encode all possible strong interactions with the muon. The ALCF, a DOE Office of Science User Facility, recently retired Mira to make room for the more powerful Aurora supercomputer, an exascale system scheduled to arrive in 2021.

“Mira was ideally suited for this work,” said James Osborn, a computational scientist with the ALCF and Argonne’s Computational Science division. ​“With nearly 50,000 nodes connected by a very fast network, our massively parallel system enabled the team to run large simulations very efficiently.”

After four years of running calculations on Mira, the researchers produced the first-ever result for the hadronic light-by-light scattering contribution to the muon anomalous magnetic moment, controlling for all errors.

“For a long time, many people thought this contribution, because it was so challenging, would explain the discrepancy,” Blum said. ​“But we found previous estimates were not far off, and that the real value cannot explain the discrepancy.”

Meanwhile, a new version of the Muon g-2 experiment is underway at Fermi National Accelerator Laboratory [above], aiming to reduce uncertainty on the experimental side by a factor of four. Those results will add more insight to the theoretical work being done now.

“As far as we know, the discrepancy still stands,” Blum said. ​“We are waiting to see whether the results together point to new physics, or whether the current Standard Model is still the best theory we have to explain nature.”

See the full article here .

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Please help promote STEM in your local schools.

Stem Education Coalition

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

The Advanced Photon Source at Argonne National Laboratory is one of five national synchrotron radiation light sources supported by the U.S. Department of Energy’s Office of Science to carry out applied and basic research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels, provide the foundations for new energy technologies, and support DOE missions in energy, environment, and national security. To learn more about the Office of Science X-ray user facilities, visit http://science.energy.gov/user-facilities/basic-energy-sciences/.

Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

From University of Chicago: “Supercomputers, giant accelerators lend a hand in the fight against coronavirus”

From University of Chicago

Researchers are using Argonne’s Advanced Photon Source, a massive kilometer-long accelerator, to analyze proteins that could lead to coronavirus treatments or vaccines. Photo courtesy of the Argonne National Laboratory.

Scientists at Argonne National Lab, UChicago search for COVID19 treatments and analysis.

As COVID-19 makes its way around the world, scientists are working around the clock to analyze the virus to find new treatments and cures and predict how it will propagate through the population.

Some of their most powerful tools are supercomputers and particle accelerators, including those at Argonne National Laboratory, a U.S. Department of Energy laboratory affiliated with the University of Chicago.

X-rays for the cure

To make drugs that work against COVID-19, we first need to find a biochemical “key”—an inhibitor molecule that will nestle perfectly into the nooks and crannies of one or more of the 28 proteins that make up the virus. While researchers have already sequenced the genes of the virus, they also need to know what the shape of each protein looks like when it is fully assembled.

This requires a technique called macromolecular X-ray crystallography, in which scientists grow tiny crystals and then illuminate them in an incredibly high-energy X-ray beam to get a snapshot of its physical structure. Such X-ray beams exist only at a few specialized sites around the world, and one of them is Argonne’s Advanced Photon Source.

By mid-March, researchers from around the country had used the Advanced Photon Source to characterize roughly a dozen proteins from SARS-CoV-2. They even managed to catch glimpses of several of them with potential inhibitor molecules “in action.”

This newly mapped coronavirus protein, called Nsp15, helps the virus replicate. Image courtesy Joachimiak et al.

“The fortunate thing is that we have a bit of a head start,” said Bob Fischetti, who heads the Advanced Photon Source’s efforts in life sciences. “This virus is similar but not identical to the SARS outbreak in 2002, and 70 structures of proteins from several different coronaviruses had been acquired using data from APS beamlines prior to the recent outbreak.”

That means researchers have background information on how to express, purify and crystallize these proteins, which makes the structures come more quickly, “right now about a few a week,” he said.

Fischetti compared finding the right inhibitor for a protein to discovering a perfectly sized and shaped Lego brick that would snap perfectly into place. “These viral proteins are like big sticky balls—we call them globular proteins,” he said. “But they have pockets or crevices inside of them where inhibitors might bind.”

By using the X-rays, scientists can gain an atomic-level view of the recesses of a viral protein and see which possible inhibitors—either pre-existing or yet-to-be-developed—might reside best in the pockets of different proteins.

The difficulty with pre-existing inhibitors is that they tend to bind only weakly to COVID-19 proteins, which might mean extremely high doses that could cause complications in patients. According to Fischetti, the research teams are looking for an inhibitor that would have a much stronger affinity, enabling it to be administered as a drug that would have many fewer or no side effects.

Fischetti said the rapid pace of collaborative science with one common essential goal is unlike anything else he has seen in his career. “Everything is just moving so incredibly fast, and there are so many moving pieces that it’s hard to keep up with,” he said.

Computing the COVID-19 crisis

Supercomputers can play a role in searching for inhibitors, too. As part of the COVID-19 High Performance Computing Consortium, researchers at Argonne and the University of Chicago are joining forces with researchers from government, academia and industry in an effort that combines the power of 16 different supercomputing systems.

Supercomputers, such as Argonne’s Theta supercomputer, can whittle down the number of possible molecules for effective treatments. Photo courtesy of the Argonne National Laboratory.

At Argonne, researchers using the lab’s Theta supercomputer have linked up with other supercomputers from around the country. With their combined might, these supercomputers are powering simulations of how billions of different small molecules from drug libraries could interface and bind with different viral protein regions.

We already have databases of many potential drug candidates—such “libraries” include catalogs of small molecules that number in the hundreds of millions to billions. The problem, then, is how to narrow them down. Running individual simulations of each and every drug candidate for each viral protein, even with the supercomputers running 24/7, would take many years—a window of time that scientists don’t have.

Luckily, this is a problem tailor-made for new AI and machine learning techniques. To zero in on the most likely candidates as efficiently as possible, computational biologists can use these techniques to do a kind of educated filtration of possibilities.

“When we’re looking at this virus, we should be aware that it’s not likely just a single protein we’re dealing with—we need to look at all the viral proteins as a whole,” said Arvind Ramanathan, a computational biologist in Argonne’s Data Science and Learning division. “By using machine learning and artificial intelligence methods to screen for drugs across multiple target proteins in the virus, we may have a better pathway to an antiviral drug.”

Ten billion configurations are quickly whittled down to roughly six million positions that researchers can then do more intensive simulations on to see which would be the best candidates.

At the end of the day, they identify a handful of inhibitor candidates that can be fed back to scientists who can then actually make these molecules, inject them into viral proteins, and then use the Advanced Photon Source to check how well the molecules work. “It’s an iterative process,” said Rick Stevens, associate laboratory director of Argonne’s Computing, Environment and Life Sciences directorate. “They feed structures to us, we feed our models to them—eventually we hope to find something that works well.”

Agents make the model

Computers can also help scientists simulate the spread of COVID-19 through the population. Argonne specializes in a kind of model called an “agent-based model.” Instead of just assuming a population of “average” people that do the same thing, agent-based models create a virtual crowd of people that act independently. The agent-based model that Argonne researchers have developed includes almost 3 million separate agents, each of whom can travel to any of 1.2 million different locations. The actions of each agent are determined by hourly schedules.

They are modifying this model to incorporate on-the-fly reports of the properties of the virus’s virulence that are being published every day in the scientific literature.

Currently, the Argonne team is developing a baseline simulation—in essence, to see what would happen to our communities if people carried on with business as usual. But the true goal is to be able to extensively model the various interventions—or possible additional interventions—that decisionmakers can implement in order to slow the virus’s spread.

“Our models simulate individuals in a city interacting with each other,” said Argonne computational scientist Jonathan Ozik, who helps to lead Argonne’s epidemiological modeling research. “If there’s a school closure, we see people who are supposed to go to school not go to school, and we can look at population level outcomes, such as how does the school closure affect how many people get exposed to the virus.”

The advantage of having a computer model of an entire city is that it represents a “laboratory” for decisionmakers to see how different decisions might affect a population without actually having to implement them. “Knowing what decisions to make on a regional or national scale and when are crucial in this worldwide fight,” said Argonne scientist and pioneer in agent-based modeling Charles (Chick) Macal, who also leads the research. “We’re developing a model that will help give information about what decisions will be most effective.”

Funding for these efforts includes support from the National Institutes of Health and the U.S. Department of Energy, among many others.

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An intellectual destination

One of the world’s premier academic and research institutions, the University of Chicago has driven new ways of thinking since our 1890 founding. Today, UChicago is an intellectual destination that draws inspired scholars to our Hyde Park and international campuses, keeping UChicago at the nexus of ideas that challenge and change the world.

The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Our commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world.

We empower individuals to challenge conventional thinking in pursuit of original ideas. Students in the College develop critical, analytic, and writing skills in our rigorous, interdisciplinary core curriculum. Through graduate programs, students test their ideas with UChicago scholars, and become the next generation of leaders in academia, industry, nonprofits, and government.

UChicago research has led to such breakthroughs as discovering the link between cancer and genetics, establishing revolutionary theories of economics, and developing tools to produce reliably excellent urban schooling. We generate new insights for the benefit of present and future generations with our national and affiliated laboratories: Argonne National Laboratory, Fermi National Accelerator Laboratory, and the Marine Biological Laboratory in Woods Hole, Massachusetts.

The University of Chicago is enriched by the city we call home. In partnership with our neighbors, we invest in Chicago’s mid-South Side across such areas as health, education, economic growth, and the arts. Together with our medical center, we are the largest private employer on the South Side.

In all we do, we are driven to dig deeper, push further, and ask bigger questions—and to leverage our knowledge to enrich all human life. Our diverse and creative students and alumni drive innovation, lead international conversations, and make masterpieces. Alumni and faculty, lecturers and postdocs go on to become Nobel laureates, CEOs, university presidents, attorneys general, literary giants, and astronauts.

From Argonne National Laboratory: “Capturing 3D microstructures in real time”

News from From Argonne National Laboratory

April 2, 2020
Joseph E. Harmon

Machine-learning based algorithm characterizes 3D material microstructure in real time.

Machine-learning enabled characterization of 3D microstructure showing grains of different sizes and their boundaries. (Image by Argonne National Laboratory.)

Modern scientific research on materials relies heavily on exploring their behavior at the atomic and molecular scales. For that reason, scientists are constantly on the hunt for new and improved methods for data gathering and analysis of materials at those scales.

Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE’s Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry.

“What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions,” said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago.

Argonne 3D machine learning algorithm shows nucleation of ice leading to the formation of nanocrystalline structure followed by subsequent grain growth. (Video by Argonne National Laboratory.)

“For example, with data analyzed by our 3D tool,” said Henry Chan, CNM postdoctoral researcher and lead author of the study, ​“users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials.”

Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains. The size and distribution of those grains and the voids within a sample are critical microstructural features that affect important physical, mechanical, optical, chemical and thermal properties. Such knowledge is important, for example, to the discovery of new materials with desired properties, such as stronger and harder machine components that last longer.

In the past, scientists have visualized 3D microstructural features within a material by taking snapshots at the microscale of many 2D slices, processing the individual slices, and then pasting them together to form a 3D picture. Such is the case, for example, with the computerized tomography scanning routine done in hospitals. That process, however, is inefficient and leads to the loss of information. Researchers have thus been searching for better methods for 3D analyses.

“At first,” said Mathew Cherukara, an assistant scientist at CNM, ​“we thought of designing an intercept-based algorithm to search for all the boundaries among the numerous grains in the sample until mapping the entire microstructure in all three dimensions, but as you can imagine, with millions of grains, that is extraordinarily time-consuming and inefficient.”

“The beauty of our machine learning algorithm is that it uses an unsupervised algorithm to handle the boundary problem and produce highly accurate results with high efficiency,” said Chan. ​“Coupled with down-sampling techniques, it only takes seconds to process large 3D samples and obtain precise microstructural information that is robust and resilient to noise.”

The team successfully tested the algorithm by comparison with data obtained from analyses of several different metals (aluminum, iron, silicon and titanium) and soft materials (polymers and micelles). These data came from earlier published experiments as well as computer simulations run at two DOE Office of Science User Facilities, the Argonne Leadership Computing Facility and the National Energy Research Scientific Computing Center. Also used in this research were the Laboratory Computing Resource Center at Argonne and the Carbon Cluster in CNM.

“For researchers using our tool, the main advantage is not just the impressive 3D image generated but, more importantly, the detailed characterization data,” said Sankaranarayanan. ​“They can even quantitatively and visually track the evolution of a microstructure as it changes in real time.”

The machine-learning algorithm is not restricted to solids. The team has extended it to include characterization of the distribution of molecular clusters in fluids with important energy, chemical and biological applications.

This machine-learning tool should prove especially impactful for future real-time analysis of data obtained from large materials characterization facilities, such as the Advanced Photon Source, another DOE Office of Science User Facility at Argonne, and other synchrotrons around the world.

This study, titled ​“Machine learning enabled autonomous microstructural characterization in 3D samples,” appeared in npj Computational Materials. In addition to Sankaranarayanan and Chan, authors include Mathew Cherukara, Troy D. Loeffler, and Badri Narayanan. This study received funding from the DOE Office of Basic Energy Sciences.

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Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

The Advanced Photon Source at Argonne National Laboratory is one of five national synchrotron radiation light sources supported by the U.S. Department of Energy’s Office of Science to carry out applied and basic research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels, provide the foundations for new energy technologies, and support DOE missions in energy, environment, and national security. To learn more about the Office of Science X-ray user facilities, visit http://science.energy.gov/user-facilities/basic-energy-sciences/.

Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

From Fermi National Accelerator Lab: “Particle accelerator technology could solve one of the most vexing problems in building quantum computers”

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.

February 26, 2020
Jerald Pinson

Superconducting radio-frequency cavities, such as the one seen here, are used in particle accelerators. They can also solve one of the biggest problems facing the successful development of a quantum computer: the decoherence of qubits. Photo: Reidar Hahn, Fermilab.

Last year, researchers at Fermilab received over $3.5 million for projects that delve into the burgeoning field of quantum information science. Research funded by the grant runs the gamut, from building and modeling devices for possible use in the development of quantum computers to using ultracold atoms to look for dark matter.

For their quantum computer project, Fermilab particle physicist Adam Lyon and computer scientist Jim Kowalkowski are collaborating with researchers at Argonne National Laboratory, where they’ll be running simulations on high-performance computers.

Their work will help determine whether instruments called superconducting radio-frequency cavities, also used in particle accelerators, can solve one of the biggest problems facing the successful development of a quantum computer: the decoherence of qubits.

“Fermilab has pioneered making superconducting cavities that can accelerate particles to an extremely high degree in a short amount of space,” said Lyon, one of the lead scientists on the project. “It turns out this is directly applicable to a qubit.”

Researchers in the field have worked on developing successful quantum computing devices for the last several decades; so far, it’s been difficult. This is primarily because quantum computers have to maintain very stable conditions to keep qubits in a quantum state called superposition.

Superposition

Classical computers use a binary system of 0s and 1s – called bits – to store and analyze data. Eight bits combined make one byte of data, which can be strung together to encode even more information. (There are about 31.8 million bytes in the average three-minute digital song.) In contrast, quantum computers aren’t constrained by a strict binary system. Rather, they operate on a system of qubits, each of which can take on a continuous range of states during computation. Just as an electron orbiting an atomic nucleus doesn’t have a discrete location but rather occupies all positions in its orbit at once in an electron cloud, a qubit can be maintained in a superposition of both 0 and 1

Since there are two possible states for any given qubit, a pair doubles the amount of information that can be manipulated: 22 = 4. Use four qubits, and that amount of information grows to 24 = 16. With this exponential increase, it would take only 300 entangled qubits to encode more information than there is matter in the universe.

Qubits can be in a superposition of 0 and 1, while classical bits can be only one or the other. Image: Jerald Pinson.

Parallel positions

Qubits don’t represent data in the same way as bits. Because qubits in superposition are both 0 and 1 at the same time, they can similarly represent all possible answers to a given problem simultaneously. This is called quantum parallelism, and it’s one of the properties that makes quantum computers so much faster than classical systems.

The difference between classical computers and their quantum counterparts could be compared to a situation in which there is a book with some pages randomly printed in blue ink instead of black. The two computers are given the task of determining how many pages were printed in each color.

“A classical computer would go through every page,” Lyon said. Each page would be marked, one at a time, as either being printed in black or in blue. “A quantum computer, instead of going through the pages sequentially, would go through them all at once.”

Once the computation was complete, a classical computer would give you a definite, discrete answer. If the book had three pages printed in blue, that’s the answer you’d get.

“But a quantum computer is inherently probabilistic,” Kowalkowski said.

This means the data you get back isn’t definite. In a book with 100 pages, the data from a quantum computer wouldn’t be just three. It also could give you, for example, a 1 percent chance of having three blue pages or a 1 percent chance of 50 blue pages.

An obvious problem arises when trying to interpret this data. A quantum computer can perform incredibly fast calculations using parallel qubits, but it spits out only probabilities, which, of course, isn’t very helpful – unless, that is, the right answer could somehow be given a higher probability.

Interference

Consider two water waves that approach each other. As they meet, they may constructively interfere, producing one wave with a higher crest. Or they may destructively interfere, canceling each other so that there’s no longer any wave to speak of. Qubit states can also act as waves, exhibiting the same patterns of interference, a property researchers can exploit to identify the most likely answer to the problem they’re given.

“If you can set up interference between the right answers and the wrong answers, you can increase the likelihood that the right answers pop up more than the wrong answers,” Lyon said. “You’re trying to find a quantum way to make the correct answers constructively interfere and the wrong answers destructively interfere.”

When a calculation is run on a quantum computer, the same calculation is run multiple times, and the qubits are allowed to interfere with one another. The result is a distribution curve in which the correct answer is the most frequent response.

When waves meet, they may constructively interfere, producing one wave with a higher crest. Image: Jerald Pinson.

Waves may also destructively interfere, canceling each other so that there’s no longer any wave to speak of. Image: Jerald Pinson.

Listening for signals above the noise

In the last five years, researchers at universities, government facilities and large companies have made encouraging advancements toward the development of a useful quantum computer. Last year, Google announced that it had performed calculations on their quantum processor called Sycamore in a fraction of the time it would have taken the world’s largest supercomputer to complete the same task.

Yet the quantum devices that we have today are still prototypes, akin to the first large vacuum tube computers of the 1940s.

“The machines we have now don’t scale up much at all,” Lyon said.

There’s still a few hurdles researchers have to overcome before quantum computers become viable and competitive. One of the largest is finding a way to keep delicate qubit states isolated long enough for them to perform calculations.

If a stray photon — a particle of light — from outside the system were to interact with a qubit, its wave would interfere with the qubit’s superposition, essentially turning the calculations into a jumbled mess – a process called decoherence. While the refrigerators do a moderately good job at keeping unwanted interactions to a minimum, they can do so only for a fraction of a second.

“Quantum systems like to be isolated,” Lyon said, “and there’s just no easy way to do that.”

When a quantum computer is operating, it needs to be placed in a large refrigerator, like the one pictured here, to cool the device to less than a degree above absolute zero. This is done to keep energy from the surrounding environment from entering the machine. Photo: Reidar Hahn, Fermilab.

Which is where Lyon and Kowalkowski’s simulation work comes in. If the qubits can’t be kept cold enough to maintain an entangled superposition of states, perhaps the devices themselves can be constructed in a way that makes them less susceptible to noise.

It turns out that superconducting cavities made of niobium, normally used to propel particle beams in accelerators, could be the solution. These cavities need to be constructed very precisely and operate at very low temperatures to efficiently propagate the radio waves that accelerate particle beams. Researchers theorize that by placing quantum processors in these cavities, the qubits will be able to interact undisturbed for seconds rather than the current record of milliseconds, giving them enough time to perform complex calculations.

Qubits come in several different varieties. They can be created by trapping ions within a magnetic field or by using nitrogen atoms surrounded by the carbon lattice formed naturally in crystals. The research at Fermilab and Argonne will be focused on qubits made from photons.

Lyon and his team have taken on the job of simulating how well radio-frequency cavities are expected to perform. By carrying out their simulations on high-performance computers, known as HPCs, at Argonne National Laboratory, they can predict how long photon qubits can interact in this ultralow-noise environment and account for any unexpected interactions.

Researchers around the world have used open-source software for desktop computers to simulate different applications of quantum mechanics, providing developers with blueprints for how to incorporate the results into technology. The scope of these programs, however, is limited by the amount of memory available on personal computers. In order to simulate the exponential scaling of multiple qubits, researchers have to use HPCs.

“Going from one desktop to an HPC, you might be 10,000 times faster,” said Matthew Otten, a fellow at Argonne National Laboratory and collaborator on the project.

Once the team has completed their simulations, the results will be used by Fermilab researchers to help improve and test the cavities for acting as computational devices.

“If we set up a simulation framework, we can ask very targeted questions on the best way to store quantum information and the best way to manipulate it,” said Eric Holland, the deputy head of quantum technology at Fermilab. “We can use that to guide what we develop for quantum technologies.”

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

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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.

From Argonne National Laboratory: “Stargazing with Computers: What Machine Learning Can Teach Us about the Cosmos”

News from From Argonne National Laboratory

February 18, 2020
Shannon Brescher Shea
shannon.shea@science.doe.gov

Gazing up at the night sky in a rural area, you’ll probably see the shining moon surrounded by stars. If you’re lucky, you might spot the furthest thing visible with the naked eye – the Andromeda galaxy.

Andromeda Galaxy Adam Evans

It’s the nearest neighbor to our galaxy, the Milky Way. But that’s just the tiniest fraction of what’s out there. When the Department of Energy’s (DOE) Legacy Survey of Space and Time (LSST) Camera at the National Science Foundation’s Vera Rubin Observatory turns on in 2022, it will take photos of 37 billion galaxies and stars over the course of a decade.

Fritz Zwicky discovered Dark Matter when observing the movement of the Coma Cluster., Vera Rubin a Woman in STEM denied the Nobel, did most of the work on Dark Matter.

Fritz Zwicky from http:// palomarskies.blogspot.com

Coma cluster via NASA/ESA Hubble

Astronomer Vera Rubin at the Lowell Observatory in 1965, worked on Dark Matter (The Carnegie Institution for Science)

Vera Rubin measuring spectra, worked on Dark Matter (Emilio Segre Visual Archives AIP SPL)

Vera Rubin, with Department of Terrestrial Magnetism (DTM) image tube spectrograph attached to the Kitt Peak 84-inch telescope, 1970. https://home.dtm.ciw.edu

The Vera C. Rubin Observatory currently under construction on the El Peñón peak at Cerro Pachón Chile, a 2,682-meter-high mountain in Coquimbo Region, in northern Chile, alongside the existing Gemini South and Southern Astrophysical Research Telescopes.

The Vera C. Rubin Observatory Data Journey, Illustration by Sandbox Studio, Chicago with Ana Kova

Dark Matter Research

Universe map Sloan Digital Sky Survey (SDSS) 2dF Galaxy Redshift Survey

Scientists studying the cosmic microwave background [CMB]hope to learn about more than just how the universe grew—it could also offer insight into dark matter, dark energy and the mass of the neutrino.

[caption id="attachment_73741" align="alignnone" width="632"] CMB per ESA/Planck

Dark matter cosmic web and the large-scale structure it forms The Millenium Simulation, V. Springel et al

Dark Matter Particle Explorer China

DEAP Dark Matter detector, The DEAP-3600, suspended in the SNOLAB deep in Sudbury’s Creighton Mine

LBNL LZ Dark Matter project at SURF, Lead, SD, USA

Inside the ADMX experiment hall at the University of Washington Credit Mark Stone U. of Washington. Axion Dark Matter Experiment

The output from this huge telescope will swamp researchers with data. In those 10 years, the Vera C Rubin Observatory Camera will take 2,000 photos for each patch of the Southern Sky it covers. Each image can have up to a million objects in it.

“In terms of the scale of the data, the amount of the data, the complexity of the data, they’re well beyond any of the current data sets we have,” said Rachel Mandelbaum, a professor at Carnegie Mellon University and LSST Dark Energy Science Collaboration spokesperson. “This opens up a huge amount of discovery space.”

Scientists aren’t building the LSST Camera to just take pretty pictures. They want to identify, categorize, and measure celestial objects that can reveal information about the very structure of the universe. Understanding dark energy and other cosmological mysteries requires data on supernovae and galaxies. Researchers may even find entirely new classes of objects.

“There are going to be some objects that we have never seen before because that is the point of new discovery,” said Renée Hložek, an assistant professor of astrophysics at the University of Toronto, who works with the LSST Dark Energy Science Collaboration. “We will find a bunch of what we call weirdos, or anomalies.”

The sheer volume and strangeness of the data will make it difficult to analyze. While a stargazer new to an area might go out in the field with a local expert, scientists don’t have such a guide to new pieces of the universe. So they’re making their own. More accurately, they’re making many different guides that can help them identify and categorize these objects. Astrophysicists supported by the DOE Office of Science are developing these guides in the form of computer models that rely on machine learning to examine the Vera C Rubin Observatory data. Machine learning is a process where a computer program learns over time about the relationships in a set of data.

Computer Programs that Learn

Processing data quickly is a must for scientists in the Dark Energy Science Collaboration.

Dark Energy Survey

Dark Energy Camera [DECam], built at FNAL

NOAO/CTIO Victor M Blanco 4m Telescope which houses the DECam at Cerro Tololo, Chile, housing DECam at an altitude of 7200 feet

Timeline of the Inflationary Universe WMAP

The Dark Energy Survey (DES) is an international, collaborative effort to map hundreds of millions of galaxies, detect thousands of supernovae, and find patterns of cosmic structure that will reveal the nature of the mysterious dark energy that is accelerating the expansion of our Universe. DES began searching the Southern skies on August 31, 2013.

According to Einstein’s theory of General Relativity, gravity should lead to a slowing of the cosmic expansion. Yet, in 1998, two teams of astronomers studying distant supernovae made the remarkable discovery that the expansion of the universe is speeding up. To explain cosmic acceleration, cosmologists are faced with two possibilities: either 70% of the universe exists in an exotic form, now called dark energy, that exhibits a gravitational force opposite to the attractive gravity of ordinary matter, or General Relativity must be replaced by a new theory of gravity on cosmic scales.

DES is designed to probe the origin of the accelerating universe and help uncover the nature of dark energy by measuring the 14-billion-year history of cosmic expansion with high precision. More than 400 scientists from over 25 institutions in the United States, Spain, the United Kingdom, Brazil, Germany, Switzerland, and Australia are working on the project. The collaboration built and is using an extremely sensitive 570-Megapixel digital camera, DECam, mounted on the Blanco 4-meter telescope at Cerro Tololo Inter-American Observatory, high in the Chilean Andes, to carry out the project.

Over six years (2013-2019), the DES collaboration used 758 nights of observation to carry out a deep, wide-area survey to record information from 300 million galaxies that are billions of light-years from Earth. The survey imaged 5000 square degrees of the southern sky in five optical filters to obtain detailed information about each galaxy. A fraction of the survey time is used to observe smaller patches of sky roughly once a week to discover and study thousands of supernovae and other astrophysical transients.

“There are going to be some objects that we have never seen before because that is the point of new discovery,” said Renée Hložek, an assistant professor of astrophysics at the University of Toronto, who works with the LSST Dark Energy Science Collaboration. “We will find a bunch of what we call weirdos, or anomalies.”

The sheer volume and strangeness of the data will make it difficult to analyze. While a stargazer new to an area might go out in the field with a local expert, scientists don’t have such a guide to new pieces of the universe. So they’re making their own. More accurately, they’re making many different guides that can help them identify and categorize these objects. Astrophysicists supported by the DOE Office of Science are developing these guides in the form of computer models that rely on machine learning to examine the LSST data. Machine learning is a process where a computer program learns over time about the relationships in a set of data.

Computer Programs that Learn

Processing data quickly is a must for scientists in the Dark Energy Science Collaboration. Scientists need to know that the camera is pointing at exactly the right place and taking data correctly each time. This quick processing also helps them know if anything has changed in that part of the sky since the last time they took photos of it. Subtracting the current photo from previous ones shows them if there’s a sign of an interesting celestial object or phenomenon.

They also need to combine a lot of photos together in a way that’s accurate and usable. This project is looking into the depths of the universe to capture images of some of the faintest stars and galaxies. It will also be taking photos in less-than-ideal atmospheric conditions. To compensate, scientists need programs that can combine images together to improve clarity.

Machine learning can tackle these challenges in addition to handling the sheer amount of data. As these programs analyze more data, the more accurate they become. Just like a person learning to identify a constellation, they gain better judgement over time.

“Many scientists regard machine learning as the most promising option for classifying sources based on photometric measurements (measurements of light intensity),” said Eve Kovacs, a physicist at DOE’s Argonne National Laboratory.

But machine learning programs need to teach themselves before they can tackle a pile of new data. There are two main ways to “train” a machine learning program: unsupervised and supervised.

Unsupervised machine learning is like someone teaching themselves about stars from just their nightly observations. The program trains itself on unlabeled data. While unsupervised machine learning can group images and identify outliers, it can’t categorize them without a guidebook of some sort.

Supervised machine learning is like a newbie relying on a guidebook. The researchers feed it a massive set of data that is labeled with the classes of each object. By examining the data over and over, the program learns the relationship between the observation and the labels. This technique is especially useful for classifying objects into known groups.

In some cases, the researchers also feed the program a specific set of features to look for, like brightness, shape, or color. They provide guidance on how important each feature is compared to the others. In other programs, the machine learning program figures out the relevant features itself.

However, the accuracy of supervised machine learning depends on having a good training set, with all of the diversity and variability of a real one. For photos from the LSST Camera, that variability could include streaks from satellites moving across the sky. The labeling also has to be extremely accurate.

“We have to put as much physics as we can into the training sets,” said Mandelbaum. “It doesn’t remove from us the burden to understand the physics. It just moves it into a different part of the problem.”

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Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

The Advanced Photon Source at Argonne National Laboratory is one of five national synchrotron radiation light sources supported by the U.S. Department of Energy’s Office of Science to carry out applied and basic research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels, provide the foundations for new energy technologies, and support DOE missions in energy, environment, and national security. To learn more about the Office of Science X-ray user facilities, visit http://science.energy.gov/user-facilities/basic-energy-sciences/.

Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

From University of Chicago and Argonne Labs: “Argonne, UChicago scientists take important step in developing national quantum internet”

From University of Chicago

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News from From Argonne National Laboratory

Scientists from Argonne and the University of Chicago entangled photons across a 52-mile network, an important step in developing a national quantum internet. Illustration by Peter Allen.

Feb 19, 2020

Real-world experiment in Chicago suburbs achieves quantum entanglement across 52-mile fiber network.

Scientists from Argonne National Laboratory and the University of Chicago entangled photons across a 52-mile network in the Chicago suburbs, an important step in developing a national quantum internet.

The quantum loop, spearheaded by UChicago professor and Argonne senior scientist David Awschalom, ran its first successful entanglement experiments on Feb. 11. Headquartered at Argonne, the loop is among the longest land-based quantum networks in the nation.

The experiment, funded by the U.S. Department of Energy’s Office of Science Basic Energy Sciences, is seen as a foundational building block in the development of a quantum internet— potentially a highly secure and far-reaching network of quantum computers and other quantum devices. A quantum internet could catalyze technologies that greatly accelerate today’s internet, significantly improve the security of communications, and support dramatic advances in computing and sensing. Scientists say quantum technology could revolutionize national and financial security, patient privacy, drug discovery, and the design and manufacturing of new materials, while increasing our scientific understanding of the universe.

“This is an important step forward in harnessing entanglement and building a network to help form the basis of future quantum communication systems,” said Awschalom, the Liew Family Professor in the Pritzker School of Molecular Engineering at UChicago, senior scientist in the Materials Science Division at Argonne and director of the Chicago Quantum Exchange. “We are excited by these initial demonstrations of distributing entanglement outside of a laboratory, as well as having a flexible communications platform that allows us to identify the challenges of translating quantum phenomena to the real world.”

In the subatomic quantum world, particles can become entangled, sharing their states even though they’re in different locations—a phenomenon which could be used to transfer information. The network, which originates at Argonne in Lemont, Ill. and winds circuitously in a pair of 26-mile loops through several of Chicago’s western suburbs, taps the unique properties of quantum mechanics to eventually “teleport” information virtually instantaneously across a distance. As a bonus, scientists believe the information would be extremely difficult to hack: Quantum states change when observed, so the presence of an outside listener would actually change the signal itself.

“By integrating communities with our national lab system, we can look forward to a future filled with innovation and collaboration,” said Paul Dabbar, the Department of Energy Under Secretary for Science. “The Department of Energy is proud to bring businesses closer to the technology built at Argonne. By developing the quantum loop, we will remain globally competitive around the world.”

The White House on Feb. 11 announced funding and a strategic vision for quantum networks in the United States. The plan envisions companies and national laboratories over the next five years working together to demonstrate the foundational science and key technologies to enable quantum networks, and over the next 20 years, quantum internet links that enable new capabilities not possible with classical technology.

Argonne plans to scale this network by developing a two-way quantum link network with UChicago-affiliated Fermi National Accelerator Laboratory. Such a link could help to lay the foundation for a national laboratory-led cross-country quantum internet.

“Both locally and nationally, Argonne fosters important industry partnerships that accelerate technology innovation and commercialization,” said Argonne Director Paul Kearns. As part of the Chicago Quantum Exchange, we create pioneering collaborations with industry, which is key to maintaining U.S. leadership in this important emerging field.”

Though quantum technology holds a great deal of promise, it’s mostly theoretical right now; quantum systems are extremely sensitive to interference and to date have been mainly tested in clean, controlled lab environments. This experiment instead runs through an existing underground network of optical fiber, built decades ago for conventional telecommunications.

University of Chicago and Argonne scientist David Awschalom (center) discusses quantum entanglement with Department of Energy Under Secretary for Science Paul Dabbar (right middle) and other laboratory, DOE and University leaders and researchers. Photo courtesy of Argonne National Laboratory

“In the real world, the fiber cables are expanding and contracting as the temperature changes. There is also vibration and noise from the environment such as local traffics,” said Tian Zhong, assistant professor of molecular engineering at UChicago and Argonne scientist in the Nanoscience and Technology Division. “These are all factors that can affect the quantum signal transmission, and that we can only find out by performing an experiment of this magnitude under real-world operating conditions.”

“Many tests of quantum technologies are confined to a research environment,” said Alan Dibos, Argonne assistant scientist in the Nanoscience and Technology Division. “One of the exciting aspects of this project is the expansion of our laboratory into the greater Chicago area.”

In achieving this milestone, Awschalom and team worked closely with companies in the emerging quantum industry. In partnership with Qubitekk, a new company developing quantum technologies, the team created entangled photon pairs and distributed them across two 26-mile fiber loops. The returning photon pairs were detected, and their correlation was verified with a high signal-to-noise ratio.

The result is the latest from members of the Chicago Quantum Exchange, a national leader in quantum information science. The exchange, includes more than 130 members from universities and national laboratories, including founding members UChicago, Argonne, Fermilab and the University of Illinois at Urbana-Champaign. The exchange also includes several non-profit and international partners and seven corporate partners, all of which have expertise in varying areas of quantum information technology.

The quantum loop is supported by the U.S. Department of Energy’s Office of Science. Additional support for this experiment was provided by the Joint Task Force Initiative, a University of Chicago program dedicated to helping Argonne and Fermilab achieve mission success.

See the full article here .

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Please help promote STEM in your local schools.

Stem Education Coalition

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

The Advanced Photon Source at Argonne National Laboratory is one of five national synchrotron radiation light sources supported by the U.S. Department of Energy’s Office of Science to carry out applied and basic research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels, provide the foundations for new energy technologies, and support DOE missions in energy, environment, and national security. To learn more about the Office of Science X-ray user facilities, visit http://science.energy.gov/user-facilities/basic-energy-sciences/.

Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

An intellectual destination

One of the world’s premier academic and research institutions, the University of Chicago has driven new ways of thinking since our 1890 founding. Today, UChicago is an intellectual destination that draws inspired scholars to our Hyde Park and international campuses, keeping UChicago at the nexus of ideas that challenge and change the world.

The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Our commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world.

We empower individuals to challenge conventional thinking in pursuit of original ideas. Students in the College develop critical, analytic, and writing skills in our rigorous, interdisciplinary core curriculum. Through graduate programs, students test their ideas with UChicago scholars, and become the next generation of leaders in academia, industry, nonprofits, and government.

UChicago research has led to such breakthroughs as discovering the link between cancer and genetics, establishing revolutionary theories of economics, and developing tools to produce reliably excellent urban schooling. We generate new insights for the benefit of present and future generations with our national and affiliated laboratories: Argonne National Laboratory, Fermi National Accelerator Laboratory, and the Marine Biological Laboratory in Woods Hole, Massachusetts.

The University of Chicago is enriched by the city we call home. In partnership with our neighbors, we invest in Chicago’s mid-South Side across such areas as health, education, economic growth, and the arts. Together with our medical center, we are the largest private employer on the South Side.

In all we do, we are driven to dig deeper, push further, and ask bigger questions—and to leverage our knowledge to enrich all human life. Our diverse and creative students and alumni drive innovation, lead international conversations, and make masterpieces. Alumni and faculty, lecturers and postdocs go on to become Nobel laureates, CEOs, university presidents, attorneys general, literary giants, and astronauts.