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June 9, 2022

SLAC’s Auralee Edelen and Noah Kurinsky receive prestigious DOE Early Career Awards

SLAC’s Auralee Edelen and Noah Kurinsky receive prestigious DOE Early Career Awards

Edelen draws on machine learning to fine tune particle accelerators, while Kurinsky develops dark matter detectors informed by quantum information science.
By David Krause and Nathan Collins

Structured Content

Auralee Edelen and Noah Kurinsky from the Department of Energy’s SLAC National Accelerator Laboratory will receive prestigious Early Career Research Program awards for work that transforms the way scientists tune particle accelerator experiments and the detectors that physicists will use to hunt for faint signs of dark matter.

The SLAC scientists are among 83 awardees from universities and national laboratories across the U.S. and will each receive $500,000 annually for five years.

"Supporting America’s scientists and researchers early in their careers will ensure the U.S. remains at the forefront of scientific discovery and develops the solutions to our most pressing challenges,” said U.S. Secretary of Energy Jennifer M. Granholm. “The funding announced today will allow the recipients the freedom to find the answers to some of the most complex questions as they establish themselves as experts in their fields.

JoAnne Hewett, SLAC’s chief research officer and associate lab director for fundamental physics, said, “The Early Career Research Program is highly successful in supporting promising young scientists to launch spectacular careers. It’s tremendous to have two of our exceptional researchers working in the cutting-edge fields of machine learning and quantum information science detector development achieve this recognition. It’s truly reflective of the depth of talent at SLAC and our promise for the future."

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Side by side photographs of a woman and a man.
2022 DOE Early Career Award winners Auralee Edelen and Noah Kurinsky. (Jacqueline Ramseyer Orrell/SLAC National Accelerator Laboratory)

Auralee Edelen: Tuning accelerators with machine learning

Auralee Edelen came to SLAC in 2018 after studying machine learning techniques and particle accelerator control as a graduate student researcher at Fermilab. Her work focuses on how to predict and customize particle accelerator beams more precisely using machine learning – an area of research the accelerator community has only just started to put serious effort into, she said.

While researchers have plenty of data, models and methods for optimizing accelerator performance, "we haven’t yet put these all together for comprehensive accelerator tuning,” Edelen said. “From tests so far we know that machine learning techniques have a lot of potential to tune particle beams more precisely and more quickly than we have been able to do before.”

When she first started studying accelerator physics at Colorado State University, the idea that machine learning could be applied to accelerators was relatively new. At the time, standard tuning procedures seemed like enough to many in the field. But now, researchers are looking for more precise control over beams, especially for newer applications in science and medicine that require unprecedented beam characteristics and stability. 

Edelen enjoys the process of blending academic disciplines to pursue solutions to difficult problems like these, she said.

“I’m interested in what can happen at the boundaries between seemingly disparate fields of study, from machine learning to classical control theory to particle accelerator science. Even philosophy and psychology have interesting perspectives to ponder for machine learning,” Edelen said. Researchers can get better results, she said, "by looking beyond your field and seeing how people with different academic backgrounds have approached similar challenges."

The award will allow Edelen to dive deeper into fundamental questions of machine learning applied to accelerators, such as whether some of the traditionally more data-intensive techniques can be applied generally at many types of facilities, whether they accelerate electrons or protons or something else. 

Zhirong Huang, her supervisor and director of SLAC’s Accelerator Research Division, said, “Auralee has been at the forefront of applying machine learning techniques to particle accelerators since her PhD work. We are very fortunate to have her in our accelerator research division and leading the effort.”

Noah Kurinsky: Qubits for dark matter detection

Kurinksy's research at SLAC goes back almost a decade when he was graduate student at Stanford. There he worked with KIPAC scientist Richard Partridge on detectors for the SuperCDMS project, an underground dark matter experiment expected to begin operating in the next year. 

Sensors for such experiments often work by detecting electrons freed from atoms in a crystal, such as silicon or diamond. These electrons interact with the surrounding crystal, producing vibrations in the crystal called phonons. Then, a sensor affixed to the crystal detects the phonons. By the time he'd finished his PhD in 2018, Kurinsky had developed a detector based on that principle that could detect a single electron generated in the crystal.

Kurinsky spent the next three years at Fermilab as a Lederman Fellow, which he said gave him the freedom to explore a range of different ideas and projects, including CCD detectors for astronomy and an experiment aimed at detecting axions, which have been proposed as an explanation for dark matter.

That experiment exposed him to the idea of using a version of qubits – the basic building blocks of quantum computers – as detectors. The ideal qubit would not be sensitive at all to electrons or other forms of radiation that might strike them. But by studying how to make them more robust, Kurinsky explained, researchers can also figure out how to make them more sensitive. In a sense, breaking the qubit turns it into an exquisitely sensitive particle detector.

"When I applied to SLAC, I said I want to come here and see how I can destroy qubits and make them into sensors," Kurinsky said. 

With his Early Career Award, Kurinsky said he’s excited to begin that work. In the first year, he plans to build his lab and work with other labs to get accustomed to making qubit-based detectors and try out small changes, such as increasing sensor sizes to make them more sensitive to phonons or applying electromagnetic fields to the crystals to gather more electrons. Then, he and his lab will work on fabricating and testing their designs at SLAC. 

"This is a very hard problem," Kurinsky said, but "by the end of the five-year program we could have a much better idea of the challenges remaining, and at that point it's more of an engineering problem than a physics problem."

Richard Partridge, a senior staff scientist at SLAC and senior member of the Kavli Institute of Particle Astrophysics and Cosmology who advised Kurinsky during his graduate studies, said, “Noah has been a leader in pushing the low-mass dark matter frontier, starting with his Ph.D. work at Stanford and continuing at Fermilab. He now leads a two-pronged effort that utilizes cryogenic technologies in both the search for dark matter and in the rapidly growing quantum information field. It is great to see these efforts recognized by an Early Career Award.”

For questions or comments, contact the SLAC Office of Communications at communications@slac.stanford.edu.


SLAC is a vibrant multiprogram laboratory that explores how the universe works at the biggest, smallest and fastest scales and invents powerful tools used by scientists around the globe. With research spanning particle physics, astrophysics and cosmology, materials, chemistry, bio- and energy sciences and scientific computing, we help solve real-world problems and advance the interests of the nation.

SLAC is operated by Stanford University for the U.S. Department of Energy’s Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time.