Scientists travel to Fermilab to discuss future of high-energy physics

More than 500 scientists and researchers from around the world recently gathered at the U.S. Department of Energy’s Fermi National Accelerator Laboratory for the 2026 American Physical Society Division of Particles and Fields meeting. Over the course of the event, held July 20-24, participants shared scientific results, explored emerging research opportunities and considered the long-term future of high-energy physics.

Fermilab was proud to host DPF 2026, which represented the largest gathering of the U.S. particle physics community this year. Patricia McBride, Fermilab’s deputy director for science and chief research officer, played a key role as a member of the APS DPF program committee, which developed the conference’s scientific program. Fermilab staff supported the event throughout the week, helping create a seamless and welcoming experience for visiting scientists and researchers.

“We are the main lab in the U.S. for particle physics, so it’s natural that the meeting is happening here at Fermilab, where there are current and future experiments that will be the central part of the U.S. effort,” said McBride.

“We are the main lab in the U.S. for particle physics, so it’s natural that the meeting is happening here at Fermilab, where there are current and future experiments that will be the central part of the U.S. effort.”

For Fermilab Director Norbert Holtkamp, the meeting came at an important moment for high-energy physics. In his opening remarks, he encouraged the community to think beyond today’s experiments and look at the long-term scientific questions that should shape the field’s future.

“We need to agree on the most important questions first and then build the instruments to answer them, not the other way around,” Holtkamp said. He also challenged the community to consider how particle physics can effectively establish shared priorities and communicate its value to governments, sponsors and the public. “Who speaks for high energy physics, and who are they speaking to?” he asked.  “Who has the mandate to speak, and where does it come from? This is the ultimate question, especially in the fast-changing environment of today.”

This year’s meeting came at what Holtkamp described as an opportune time, with two of particle physics’ most ambitious scientific programs set to come online in the coming decade. These include the Deep Underground Neutrino Experiment at the Long Baseline Neutrino Facility, hosted by Fermilab, and the High-Luminosity upgrade to CERN’s Large Hadron Collider, to which Fermilab is contributing technology and expertise. Together, they will provide unprecedented opportunities to investigate neutrinos, the Higgs boson and some of the universe’s most fundamental mysteries.

The conference also highlighted the global nature of the field. During the opening evening, CERN Director-General Mark Thomson delivered a public lecture in Ramsey Auditorium at Fermilab’s iconic Wilson Hall, describing how the next generation of major scientific facilities will build on decades of discovery.

The event concluded with a panel discussion featuring several of the field’s influential leaders, including Fermilab Director Norbert Holtkamp, former Fermilab Director Pier Oddone, former CERN Director-General Fabiola Gianotti and former Fermilab and Lawrence Berkeley National Laboratory Director Mike Witherell, who moderated the conversation.  Their discussion explored the future of particle physics, from the technologies emerging from fundamental research to the scientific priorities that will shape the next generation of discovery.

Looking ahead, Holtkamp emphasized the importance of continuing to lead technological innovation. “We need to be at the front of that wave, not behind it,” he said.

A year after their final muon magnetic anomaly announcement, the Muon g-2 collaboration today announced a new measurement of a different property of the muon: its electric dipole moment. Based on an analysis of 25% of Fermilab’s experimental data, this is the most sensitive direct search for a muon EDM ever accomplished. It is the first direct search for the muon EDM done at the U.S. Department of Energy’s Fermi National Accelerator Laboratory and only the third search globally in the last 50 years.

Searches for EDMs play a vital role in particle physics; detecting an EDM could be key to better understanding the matter-antimatter asymmetry required to explain the universe we see around us. This new result shows that if a muon EDM exists, it must be smaller than what the Muon g-2 experiment can currently detect.

Fermilab has hosted the Muon g-2 experiment and collaboration since 2008. The experiment is made up of a 50-foot-diameter superconducting magnetic storage ring repurposed from an earlier version of the experiment at DOE’s Brookhaven National Laboratory, which concluded in 2001. The Fermilab experiment improves upon the Brookhaven version in numerous ways, enabling more precise measurements.

The Muon g-2 experiment sends a beam of muons — technically their antimatter counterparts, anti-muons or positive muons — into the storage ring, where they circulate hundreds of times at nearly the speed of light before they decay. Detectors lining the ring observe the decay products and allow scientists to determine how fast the muons are precessing, or wobbling, in the presence of a magnetic field. The precession speed is related to a property of the muon called the magnetic dipole moment, represented by the letter g. Theory predicts that g should be slightly larger than 2.

The electric dipole moment is a property that describes the separation of positive and negative charge within a system. The Muon g-2 collaboration can search for it almost for free since it uses the same data as the magnetic dipole moment analyses: while the g-2 measurement utilizes the muon’s horizontal wobble caused by the magnetic field, the muon EDM refers to a vertical component of the wobble caused by what the muon experiences as an electric field.

“If you set it all up and you tune all of the parameters of the experiment to measure the magnetic dipole moment as well as possible, there’s also, coincidentally, some sensitivity to the electric dipole moment,” said Joe Price, a co-lead of the EDM analysis from the University of Liverpool.

An indicator of new physics

The Muon g-2 experiment stores muons traveling at nearly the speed of light. They travel so fast that, due to Einstein’s theory of relativity, their typical lifetimes of 2.2 microseconds are boosted by a factor of nearly 30. This boost also enhances the electric field and, hence, the experiment’s sensitivity to the muon EDM, which allows scientists to look for new physics in a way for which the experiment wasn’t initially intended.

The Standard Model of particle physics predicts that fundamental particles, like muons, have an EDM so tiny that it could not be detected by an experiment. Therefore, measuring a non-zero muon EDM would indicate new physics.

Specifically, a non-zero EDM would violate fundamental symmetries in physics. Evidence of charge-parity violation could explain one of the biggest mysteries in the universe: why the universe is only made of matter and no antimatter is left today.

“The primary measurement of the g-2 experiment is sensitive to new physics unrelated to the matter-antimatter asymmetry,” said Gavin Hesketh, EDM analysis co-lead from University College London. “The electric dipole moment search gives us this sensitivity.”

The trackers: ‘an absolute necessity‘

The EDM analysis is possible thanks to two detectors called “trackers” inside the storage ring.

The trackers comprise 32 layers of aluminum-coated mylar straws that register the passages of charged particles. Like a connect-the-dots picture, the data from the trackers show locations of anti-muons and their decay products — positrons, the antimatter counterparts of electrons — in the ring. The muon EDM search requires knowing the difference between the numbers of positrons going up versus down, for which the trackers are indispensable.

The tracker’s design and prototyping was funded by a DOE Early Career Research Award given to Fermilab scientist Brendan Casey in 2012. A grant from the U.K. Science and Technology Facilities Council funded their construction.

Casey and Fermilab collaborated on the trackers with U.S. and U.K. institutions, including Boston University, Northern Illinois University, the University of Liverpool and University College London. They even received help with prototyping and quality control from students at the Illinois Mathematics and Science Academy, a college prep school in Aurora.

The work on the trackers paid off. As soon as the collaboration turned on the experiment, they realized the beam didn’t meet specifications. The trackers turned out to be essential to locate the exact position of the beam and show how it was affecting the experiment.

“The trackers were essential in mapping the profile of the beam. Without them, we could not have extracted g-2 or the EDM from the data,” said Casey, now a senior scientist at Fermilab. “They really turned out to be an absolute necessity.”

Next steps

In the last 50 years, there have only been two direct measurements of the muon EDM: first from CERN in 1978, then from the Muon g-2 experiment at Brookhaven in 2009.

This latest result of d_m = (-0.35 ± 0.39) ×10^-19 e·cm is compatible with an EDM of zero, which means the collaboration could set an upper limit of |d_m| < 1.1 × 10^-19 e·cm at 95% confidence level, about 1.5 times more stringent than that set by the Brookhaven Muon g-2 experiment.

Physicists can calculate a bound on the muon EDM by scaling up the limit on the electron EDM. Electrons are essentially lighter, stable and more abundant cousins of muons, so there are more precise upper constraints of the electron EDM. But this method does not account for sources of new physics that may affect muons differently than electrons.

This first result from the Fermilab experiment only uses 25% of its data, but this is already vastly more data than Brookhaven collected. Because of this, Fermilab’s muon EDM measurement is the most stringent direct limit on muon EDM to date. It is also a crucial reference for the next generation of experiments now under construction in Japan and Switzerland, which are designed to reach higher precision.

The Deep Underground Neutrino Experiment at the Long-Baseline Neutrino Facility recently began installing the structural elements of its massive neutrino detector modules. At the same time, the DUNE collaboration is working on integrating artificial intelligence into all facets of the experiment to improve measurements, provide rapid detection and optimize operations — ultimately empowering DUNE scientists to accelerate discovery.

“With DUNE, researchers are taking innovative approaches to try and impact every aspect of the experiment.”

Neutrino scientists at the U.S. Department of Energy’s Fermi National Accelerator Laboratory have been working at the cutting edge of AI and machine learning integration for decades. Two of the lab’s neutrino experiments, NOvA and MicroBooNE, were among the first high-energy physics experiments to use a deep neural net — a type of machine learning that’s intended to mirror the capabilities of the human brain — to identify particle interactions.

“With DUNE, researchers are taking innovative approaches to try and impact every aspect of the experiment,” said Sowjanya Gollapinni, co-spokesperson for DUNE. “These approaches are going to really accelerate DUNE in terms of the commissioning process and the sensitivity of the experiment to reach our full discovery potential.”

Tracking particle interactions

Sometimes called the ghost particles of the universe, neutrinos are incredibly elusive because they only interact through the weak force and gravity. Trillions are streaming through your body every second, but only one may interact in your entire lifetime. That’s why scientists use intense sources — like Fermilab’s accelerator complex — and massive detectors to increase the odds of capturing them. 

DUNE at LBNF will consist of a near detector located at Fermilab and a far detector installed a mile underground at the Sanford Underground Research Facility in South Dakota, creating the experiment’s long baseline. Both detectors will use liquid-argon time projection chamber technology to record the rare interactions that neutrinos will have with the argon atoms in the detector.

While liquid-argon TPCs are fantastic at capturing neutrino interactions, reconstructing the signals can get pretty tricky. This is especially true for DUNE, which will enable scientists to study the properties of neutrinos with unprecedented precision.

“We’ve had great success in implementing these tools in previous Fermilab experiments.”

“We are going to have very high resolution, almost photographic-quality images of these interactions,” said Leigh Whitehead, DUNE AI/ML co-lead. “Which is great, but also brings challenges such as making reconstruction more difficult because we see such fine detail.”

The AI algorithms used in DUNE will be able to recognize that a neutrino interaction took place inside the detector. They can then analyze the resulting particle tracks visible within the detector, trace back to where the interaction occurred, and extrapolate the energy and direction of the neutrino at the time it entered the detector.

“We’ve had great success in implementing these tools in previous Fermilab experiments,” said Jianming Bian, DUNE AI/ML co-lead. “And now DUNE is taking the leading role to further develop these algorithms and explore new AI methods that take advantage of its complex, large-scale data, making it a platform for AI/ML development.”

These AI-driven tools are orders of magnitude faster than traditional methods, leading to enhanced signal processing, event classification and particle identification. This will enable the collaboration to tackle their main science goals more efficiently and ultimately unlock the secrets of neutrinos.

Locating exploding stars

While DUNE researchers are using AI to reconstruct the more common neutrino signals within the detector, they are also training algorithms to identify rare phenomena. Referred to as triggers, these are fast decision-making systems that are trained to sort through thousands of interactions and save only the most interesting for physics.

One of the major triggers for DUNE would occur if a supernova goes off in our galaxy.

When a large enough star runs out of fuel and “dies,” it rips itself apart in an explosion that sends stellar remains flying into space. Particles like photons get trapped in the resulting gas and dust, neutrinos — which rarely interact — can breeze right through. This means that the neutrinos will arrive to Earth a few hours before the light burst, offering scientists a rare glimpse into the processes unfolding deep inside an exploding star.

The early alert from the neutrino burst also enables researchers to quickly identify where the supernova occurred so that astrophysicists know where to point their telescopes in time to see light from the event.

That’s where the trigger comes in. “An algorithm will be constantly scanning the stream of data from the detector, searching for interesting interactions,” said Thomas Junk, senior scientist at Fermilab. The instant the algorithm suspects a signal is from a supernova, “it will tell the system to record data from 10 seconds before and 100 seconds after the candidate signal to capture the full picture of neutrino interactions from this event. An AI algorithm will then run on these data to find the most likely position in the sky where the supernova burst happened.”

Aside from acting as an early warning system for astronomers to observe a supernova in real time, neutrinos may also provide crucial insight into the stellar objects left behind, either a neutron star or black hole.

Running massive detectors

Detecting neutrinos isn’t the only complicated part of DUNE, the detectors are comprised of thousands of components that need to be monitored to ensure that they’re working in concert as designed. 

After the detector is installed and commissioned, operators must actively monitor the system during data collection. If there is an error, it is critical that the team can respond quickly and knowledgably to any situation.

To better empower operators to tackle more unique scenarios, the DUNE collaboration is looking into whether a large language model can be used to quickly scan logbooks detailing successful documented fixes to provide immediate citations to solutions. Fermilab is coordinating with six other national labs to develop a similar tool for particle accelerators.

The DUNE collaboration is also looking into how machine learning can be used to automate detector operation a step further to potentially predict detector anomalies before they happen.

Setting the stage

DUNE has been actively investigating AI integration for years. But now the Department of Energy’s Genesis Mission is compounding that effort to accelerate scientific discovery.

“The Genesis Mission has strengthened the already existing collaboration across the national labs and universities on AI efforts,” said Gollapinni. “It is taking everything to the next level and will maximize our capabilities to accelerate DUNE.”

Integrating AI into the detector itself isn’t the only approach that DUNE is taking when it comes to AI. The collaboration is actively working with the DOE’s national labs and partnering institutions to enhance their existing AI infrastructure and prepare for the workflows necessary to process the petabytes (quadrillions of bytes) of data DUNE will produce.

DUNE is also involved in training the next-generation AI workforce.

“We have over a thousand people from around the globe collaborating on DUNE,” said Whitehead. “We’re training hundreds of students a year, which is a pretty impressive pipeline of people that should have all the tools to become leaders in the field.”