dimensions of particle physics Symmetry is your view into the world of particle physics. Symmetry receives funding through the US Department of Energy.
Hear the latest news, meet the people behind the science, and get the background information you need to gain fluency in the language of particle physics. Symmetry is a joint publication of Fermi National Accelerator Laboratory and SLAC National Accelerator Laboratory.
About 2,200 people took part in hands-on science activities, performances, lectures, and exhibits at Sanford Underground Research Facility's Neutrino Day celebration this year.
At Sanford Underground Research Facility's Neutrino Day celebration, about 2,200 people took part in hands-on science activities, performances, lectures, and exhibits across Lead, South Dakota.
Nobel Laureate Peter Higgs died earlier this year. Twelve years ago this week, physicists discovered a particle that beats his name.
Article by Claire Malone
Illustration by Sandbox Studios, Chicago, with Corinne Mucha
Published 07/02/24
Nobel Laureate Peter Higgs died earlier this year. Twelve years ago this week, physicists discovered the particle that bears his name.
In May 2009, NASA launched its final mission to repair and upgrade the Hubble Space Telescope. Astronaut John Grunsfeld, one of seven on the Atlantis Shuttle, carried with him some precious cargo—the wedding bands of John and Neta Bahcall.
“John [Grunsfeld] called me and asked if he could take something of John’s to the Hubble,” says Neta Bahcall. “He said, ‘John was such a hero to me, and the Hubble wouldn’t be here without him.’ He wanted to take something personal. So, I gave him our two wedding rings.”
John Bahcall had passed away just four years earlier.
It was a fitting tribute to the man who was instrumental in developing the Hubble Space Telescope, then in successfully lobbying Congress to continue servicing missions. The telescope, launched in 1990, continues to collect striking images of nebula, stars and distant galaxies. But the Hubble was just one of hundreds of projects in which Bahcall was involved. His dedication to scientific pursuit knew no bounds and cut across the field of astrophysics.
Bahcall’s scientific legacy is visible across the field of astrophysics
At the end of 2023, a panel tasked with enumerating the priorities of the US particle physics community included in its report a plan to build an even bigger, next-generation dark-matter experiment. This year in May, a representative of the Department of Energy—one of the largest funders of US particle physics—announced its support for that recommendation.
The specifics of a next-generation dark-matter detector are still to be determined, says Regina Rameika, associate director of DOE’s Office of High Energy Physics. But the vision to expand the search, she says, is clear and worth pursuing.
Article By Laura Dattaro
Published 06/18/24
Illustration by Sandbox Studio, Chicago with Corinne Mucha
At the end of 2023, a panel tasked with enumerating the priorities of the US particle physics community included in its report a plan to build an even bigger, next-generation dark-matter experiment. This year in May, a representative of the Department of Energy—one of the largest funders of US particle physics—announced its support for that recommendation.
The specifics of a next-generation dark-matter detector are still to be determined, says Regina Rameika, associate director of DOE’s Office of High Energy Physics. But the vision to expand the search, she says, is clear and worth pursuing.
Physicists are preparing for the next generation of dark-matter experiments.
You’re running down a plant-lined path, lost on a strange planetoid, when you find a logbook. It belongs to a human-like being named Dirca. The book explains Dirca’s discovery of a strange substance, which she believes came from another world made up of antimatter.
The world you discover in the video game Exographers is imaginary, but to survive, you must learn lessons based on real physics.
That’s because the idea for the game came from Raphael Granier De Cassagnac, a French physicist who works on the PHENIX experiment, hosted at the US Department of Energy’s Brookhaven National Laboratory, and the CMS experiment, hosted at European physics laboratory CERN.
Raphael Granier De Cassagnac recently put his scientific skills to use creating a physics-themed video game.
Using the LSST Camera, Rubin Observatory will fuel advances in many science areas, including exploring the nature of dark matter and dark energy, mapping the Milky Way, surveying our solar system, and studying celestial objects that change in brightness or position.
“Getting the camera to the summit was the last major piece in the puzzle,” says Victor Krabbendam, project manager for Rubin Observatory. “With all Rubin’s components physically on site, we’re on the home stretch towards transformative science with the LSST.”
The largest camera ever built for astrophysics has completed the long journey from SLAC National Accelerator Laboratory in California to the summit of Cerro Pachón in Chile.
When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she knew she was going to need some new computing skills.
As she made her way through her coursework, she took a class in the computer language Python and taught herself some coding. But when she landed an internship at MIT Lincoln Laboratory, her supervisors asked her to take on a task she had not anticipated. They asked her to identify types of stars by training a neural network, a type of machine learning that teaches computers to process data like the brain processes information.
Mastandrea had to learn on the fly. She looked up tutorials online and spent hours researching problems with her code. The work was frustrating and difficult.
At the end of the project, she was relieved to go back to learning about the universe from textbooks and equations. She thought she’d never want to use machine learning again.
“That was very wrong,” Mastandrea says. “Now I use machine learning every day.”
As a doctoral student at the University of California, Berkeley, Mastandrea is now writing algorithms that can hunt for signatures of unknown new physics in data from the Large Hadron Collider. She says that without machine learning, this type of search, called anomaly detection, would be nearly impossible.
As Mastandrea shifted from machine-learning skeptic to regular user, so too did much of the rest of physics. That meant a constant flow of interesting new challenges. “It’s a lot easier to be excited about it when everyone is working on it,” she says.
Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it?
Physics may seem like its own world, but different sectors using machine learning are all part of the same universe.
One of the first versions of AI was a computer that played chess. Developed in the 1950s, it could play a full game without the input of a human—except, of course, the moves of its opponent. It took the computer about eight minutes to make each of its own moves, but the computational breakthrough was the beginning of the end of a world without AI. Today, AI tools are taking on a variety of tasks, including helping to operate complex machines in particle physics and astrophysics.
Just as the chess-playing AI required a human opponent, modern AI systems in control rooms must work together with human operators. And just as practicing against an AI might give a human new ideas for ways to play chess, working with an AI in the laboratory might help humans find new ways to operate machines for science.
Scientists inside and outside of particle physics and astrophysics are leaning on AI for assistance with complex tasks.
Theoretical physicists employ their imaginations and their deep understanding of mathematics to decipher the underlying laws of the universe that govern particles, forces and everything in between. More and more often, theorists are doing that work with the help of machine learning.
As might be expected, the group of theorists using machine learning includes people classified as “computational” theorists. But it also includes “formal” theorists, the people interested in the self-consistency of theoretical frameworks, like string theory or quantum gravity. And it includes “phenomenologists,” the theorists who sit next to experimentalists, hypothesizing about new particles or interactions that could be tested by experiments; analyzing the data the experiments collect; and using results to construct new models and dream up how to test them experimentally.
In all areas of theory, machine-learning algorithms are speeding up processes, performing previously impossible calculations, and even causing theorists to rethink the way theoretical physics research is done.
“We’re near the very beginning of something that, to me, is an obvious revolution: the use of computers in scientific discovery,” says Jim Halverson, a professor of physics at Northeastern University. “It’s like being within 50 years of Galileo pointing his telescope at the sky for the first time. Much of the current progress utilizes machine learning.”
Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries?
Every day in August of 2019, physicist Dimitrios Tanoglidis would walk to the Plein Air Café next to the University of Chicago and order a cappuccino. After finding a table, he would spend the next several hours flipping through hundreds of thumbnail images of white smudges recorded by the Dark Energy Camera, a telescope that at the time had observed 300 million astronomical objects.
For each white smudge, Tanoglidis would ask himself a simple yes-or-no question: Is this a galaxy? “I would go through about 1,000 images a day,” he says. “About half of them were galaxies, and the other half were not.”
After about a month, Tanoglidis—who was a University of Chicago PhD student at the time—had built up a catalogue of 20,000 low-brightness galaxies.
Then Tanoglidis and his team used this dataset to create a tool that, once trained, could evaluate a similar dataset in a matter of moments. “The accuracy of our algorithm was very close to the human eye,” he says. “In some cases, it was even better than us and would find things that we had misclassified.”
The tool they created was based on machine learning, a type of software that ‘learns’ as it digests data, says Aleksandra Ciprijanovic, a physicist at the US Department of Energy’s Fermi National Accelerator Laboratory who at the time was one of Tanoglidis’s research advisors. “It’s inspired by how neurons in our brains work,” she says—adding that this added “brainpower” will be essential for analyzing exponentially larger datasets from future astronomical surveys. “Without machine learning, we’d need a small army of PhD students to give the same type of dataset.”
For more than 20 years in experimental particle physics and astrophysics, machine learning has been accelerating the pace of science, helping scientists tackle problems of greater and greater complexity.
From the field’s early days and even now, physicists have played an important role in the development of machine learning, whether through contributing theories that others have used to design machine-learning models or through developing leading-edge techniques of their own.
“Money and capitalism allow industry to move faster,” says Thea Aarrestad, a particle physicist at ETH Zurich. “But the groundwork came from us in pure research.”
Today, physicists work in a triangle of sorts—connected to both industry and computer science academics—to use these approaches to advance high-energy physics and to develop new techniques that can be used across domains.
Physicists work with computer scientists in academia and industry to advance machine learning.
It’s time for some deep learning. Check out this list to pick up some new terminology—and learn a bit about the history of artificial intelligence in particle physics and astrophysics.
Don’t know your convolutional neural networks from your boosted decision trees? Symmetry is here to help.
Physicists were some of the earliest developers and adopters of technologies now welcomed under the wide umbrella term “AI.” Particle physicists and astrophysicists, with their enormous collections of data and the need to efficiently analyze it, are just the sort of people who benefit from the automation AI provides.
So we at Symmetry, an online magazine about particle physics and astrophysics, decided to explore the topic and publish a series on artificial intelligence. We looked at the many forms AI has taken; the ways the technology has helped shape the science (and vice versa); and the ways scientists use AI to advance experimental and theoretical physics, to improve the operation of particle accelerators and telescopes, and to train the next generation of physics students. You can expect to see the result of that exploration here in the coming weeks.
In the coming weeks, Symmetry will explore the ways scientists are using artificial intelligence to advance particle physics and astrophysics—in a series of articles written and illustrated entirely by humans.
In December, the Particle Physics Project Prioritization Panel, called P5, released its recommendations for the future of the field, based on the input from the Snowmass process. Among the top priorities identified was research and development toward future accelerator technology, with a specific mention of the concept of building a muon collider in the United States.
The US physics community dreams of building a muon collider.
To study dark energy’s effects over the past 11 billion years, DESI has created the largest 3D map of our cosmos ever constructed, with the most precise measurements to date. This is the first time scientists have measured the expansion history of the young universe with a precision better than 1%, giving us our best view yet of how the universe evolved.
With just its first year of data, DESI has surpassed all previous 3D spectroscopic maps combined and confirmed the basics of our best model of the universe.
After two decades of work, scientists and engineers at the US Department of Energy's SLAC National Accelerator Laboratory and their collaborators are celebrating the completion of the Legacy Survey of Space and Time (LSST) Camera.
As the heart of the DOE- and National Science Foundation-funded Vera C. Rubin Observatory, the 3,200-megapixel camera will help researchers observe our universe in unprecedented detail. Over 10 years, it will generate an enormous trove of data on the southern night sky that researchers will mine for new insights into the universe. That data will aid in the quest to understand dark energy, which is driving the accelerating expansion of the universe, and the hunt for dark matter, the mysterious substance that makes up around 85% of the matter in the universe. Researchers also have plans to use Rubin data to better understand the changing night sky, the Milky Way galaxy, and our own solar system.
Once set in place atop a telescope in Chile, the 3,200-megapixel LSST Camera will help researchers better understand dark matter, dark energy and other mysteries of our universe.
About once every year or two, physicist Albrecht Karle faces a five-day commute. And that timeline is only if everything goes perfectly.
The University of Wisconsin–Madison professor is a co-leader of operations for the IceCube Neutrino Observatory, a massive physics experiment at the South Pole funded by the US National Science Foundation and partners. And while, yes, Karle gets most of his work done at the offices of UW’s Wisconsin IceCube Particle Astrophysics Center in Madison, he does occasionally need to get to the Pole.
The first steps on his journey are a series of commercial flights from Madison to Los Angeles to Auckland, New Zealand, and on to Christchurch. Ideally, this takes 30 hours over two calendar days. Karle stays a couple of nights in Christchurch, home of the US Antarctic Program deployment hub, to pick up extreme cold-weather gear—parkas, gloves, hats, etc.—and do any check-in procedures with USAP.
Karle’s next stop is McMurdo Station, an international research facility run by NSF on the coast of Antarctica, and USAP’s logistics headquarters. Depending on which military transport plane he takes—a Boeing C-17 or a Lockheed LC-130—it could take four or seven hours, respectively.
At McMurdo, Karle must wait for the weather to permit the final leg of the trip. “It is not uncommon to spend several days in McMurdo,” he says. (Karle’s record is 10.) When it’s time, he takes a 3.5-hour flight on a ski-equipped LC-130 aircraft to reach the South Pole. Anyone or anything else that goes to the South Pole must take a similarly tedious route.
There’s a reason scientists have endured the challenges of the climate, the commute and the cost for over half a century—since members of the US Navy completed the original Amundsen–Scott South Pole Station in 1957. Despite all the trouble it takes to get there, the South Pole is an unparalleled environment for scientific research, from climate science and glaciology to particle physics and astrophysics.
The Particle Physics Project Prioritization Panel recently recommended, among their top priorities for the next decade, moving forward with two experiments based at the South Pole.
To study some of the smallest things in the universe, particle physicists use some of the biggest experimental equipment on the planet: The Large Hadron Collider, which has a circumference of about 17 miles, for example, doesn't exactly fit on a lab bench. But researchers have limited time and money to build such projects, which means they must prioritize their efforts.
With this goal in mind, the 2023 Particle Physics Project Prioritization Panel, or P5, released a report outlining a long-term strategy for US spending in the coming decade of particle physics research.
One of the most forward-looking recommendations of the report is to invest in research and development toward “a 10 TeV parton center-of-momentum … collider to search for direct evidence and quantum imprints of new physics at unprecedented energies.”
An advisory committee recommends the US work to advance three key areas of emerging accelerator technology.
The particle accelerators that enable high-energy physics and serve many fields of science, such as materials, medical and fusion research, are driven by superconducting magnets that are, to put it simply, quite finicky.
Berkeley Lab researchers are developing an approach to avoid sudden, potentially destructive energy releases in a new generation of superconducting magnets.
Outweighing regular matter by a factor of five, but so far invisible to scientists’ experiments, dark matter is (literally) one of the biggest mysteries in our universe. A podcast from the Interactions Collaboration, Particle Mysteries, offers a new way to learn about the puzzling form of matter that holds our universe together, with a four-episode arc titled The Coldest Case.
Particle Mysteries, a podcast released by the Interactions Collaboration, illuminates the international search for dark matter through conversations with its inquirers.
At the start of his academic career, Jason Terry was primarily interested in astronomy. But while earning his master’s degree at Brown University, he veered into new territory: particle physics.
In 2018 Terry took an opportunity to analyze data for the CMS experiment at the Large Hadron Collider—and who wouldn’t? he says. The CMS experiment was instrumental in one of the most important recent discoveries in particle physics, the Higgs boson. And building the 17-mile-long underground particle collider where the CMS experiment sits is “probably one of the coolest things that people have done, ever.”
Using the focus of his degree, data science, and his experience analyzing data for astronomy, Terry worked to improve the energy reconstruction of particles passing through the CMS detector. He did this by feeding the data through a machine-learning model.
Not long afterward, while pursuing his PhD at the University of Georgia, he set out to demonstrate that the same machine-learning method could work in astronomy research as well. The effort turned out to be even more effective than he had hoped.
A scientist tried using machine-learning techniques from particle physics to analyze data from astronomy—and in the process discovered a new exoplanet.
Wan-Lin Hu is an associate staff scientist specializing in “human-in-the-loop engineering” at the Department of Energy’s SLAC National Accelerator Laboratory. The lab is home to things like particle accelerators and electric power systems that are far too complex for people to run on their own, but still need a human touch to keep them on the right track.
Wan-Lin Hu’s job is to improve the way people and artificial intelligence collaborate to run SLAC’s complex machines.
In 2011, three physicists embarked upon a journey across India. They were looking for the best place in the country to listen for the faintest sounds in the universe.
According to Tarun Souradeep, Bala Iyer and Rana Adhikari, they have found that place, in a southwestern plateau region called Aundha in the Maharashtra state.
India is set to house the next detector of the Laser Interferometer Gravitational Wave Observatory, or LIGO.
In India, scientists are building a new LIGO detector, enhancing the capabilities of the observatory that reported the first observation of gravitational waves.
It was her 31st birthday, and Jordan Glover was in a rut. She was a college drop-out with a decade of retail and customer service jobs under her belt, living paycheck-to-paycheck as a stocker at Costco.
“I didn’t have enough money to pay my bills,” she says. “I was in this cycle of always trying to find a job that pays enough, but I don’t have any qualifications, my skillset was a general skillset. I was very financially unstable and tired of being in that situation.”
Glover had always been bright and ambitious. For her 4th grade science fair, she spent several mornings hiking to nearby houses with a blood pressure cuff to measure the effects of coffee on adults’ vital signs. “I got positive results,” she says.
Both of Glover’s parents work in the medical field, and she assumed she would similarly wind up in STEM. She enrolled in Tougaloo College—a historically Black college in Jackson, Mississippi—to study chemistry, but something didn’t click. Suddenly, “I just wasn’t motivated,” she says.
At the age of 19, Glover dropped out in search of a different path.
Now in her 30s, Glover found herself again feeling lost—until her sister gave her a pep talk.
A former retail worker finds the confidence to pursue a career in STEM thanks to an internship program designed for students at small colleges.
Excavation workers have finished carving out the future home of the gigantic particle detectors for the international Deep Underground Neutrino Experiment. Located a mile below the surface, the three colossal caverns are at the core of a new research facility that spans an underground area about the size of eight soccer fields.
The excavation of the caverns that will house the gigantic particle detectors of the Deep Underground Neutrino Experiment in Lead, South Dakota, is complete.
"According to [Cristian Peña, the convener of the CMS exotic particles group and a scientist at the US Department of Energy’s Fermi National Accelerator Laboratory], they will either find new physics, or set the most stringent limits in the search for long-lived particles: a class of theoretical particles that can travel deep into the detector before creating visible signals."
CMS scientists are analyzing the first dataset gathered through a new tool designed to search for long-lived particles.
A highly technical and delicate piece of equipment weighing 27,500 pounds (or 12,500 kilograms) just made a whirlwind transatlantic trip in its custom transportation frame.
The United Kingdom will eventually contribute three assembled cryomodules—known as HB650 for the radio frequency they use to operate—to Fermilab’s new particle accelerator.
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For the Muon g-2 experiment, researchers create billions of muons to study their surprising properties.
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