Research could help solve confusing proton puzzle
Protons are tiny but they carry a lot of weight. They inhabit the center of every atom in the universe and play a vital role in one of the most powerful forces in nature. And yet, protons also have a down-to-earth side.
Like most particles, protons have spin which acts like tiny magnets. Reversing the spin or polarity of a proton may sound like science fiction, but it’s the basis for technological breakthroughs that have become essential to our daily lives, such as magnetic resonance imaging (MRI), the diagnostic tool invaluable medicine.
Despite these advances, the inner workings of the proton remain a mystery.
“Basically, everything around you exists because of protons – and yet we still don’t understand everything about them. A huge puzzle that physicists want to solve is the spin of the proton. said Ben Nachman, a physicist who leads the Machine Learning Group in the Physics Division of the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab).
Understanding how and why proton spin could lead to technological advances we can’t even imagine today, and help us understand the strong force, a fundamental property that gives all protons and therefore all atoms a mass.
But this is not such an easy problem to solve. For one thing, you can’t exactly take a proton and put it in a petri dish: protons are incredibly small – their radius is a hair of a quadrillionth of a meter, and visible light passes through them. Moreover, you cannot even observe their interiors with the most powerful electron microscopes in the world.
Recent work by Nachman and his team could bring us closer to solving this baffling proton riddle.
As a member of the H1 collaboration – an international group that now includes 150 scientists from 50 institutes and 15 countries, and is based at the DESY national research center in Germany – Nachman has developed new machine learning algorithms to accelerate the analysis of the collected data. decades ago by HERA, the world’s most powerful electron-proton collider which operated at DESY from 1992 to 2007.
HERA – a ring 4 miles in circumference – operated like a giant microscope that accelerated both electrons and protons to almost the speed of light. The particles collided head-on, which could scatter a proton into its building blocks: quarks and gluons.
HERA scientists took measurements of the particle debris from these electron-proton collisions, what physicists call “deep inelastic scattering”, using sophisticated cameras called particle detectors, one of which was the detector. H1.
Unveiling the secrets of the strong force
The H1 stopped collecting data in 2007, the year HERA was dismantled. Today, the H1 collaboration continues to analyze the data and publish the results in scientific journals.
This can take a year or more when using conventional computational techniques to measure quantities related to proton structure and the strong force, such as the number of particles produced when a proton collides with an electron.
And if a researcher wants to look at a different quantity, like the speed at which particles fly in the wake of a quark-gluon jet stream, they’ll have to go through the long calculation process again and wait another year.
A new machine learning tool called OmniFold — which Nachman co-developed — can simultaneously measure multiple quantities at once, reducing the time to run an analysis from years to minutes.
OmniFold does this by using both neural networks to combine computer simulations with data. (A neural network is a machine learning tool that processes complex data that scientists cannot do manually.)
Nachman and his team first applied OmniFold to experimental H1 data in a June issue of the journal Physical Review Letters and most recently at the 2022 Deep Inelastic Scattering (DIS) conference.
To develop OmniFold and test its robustness against H1 data, Nachman and Vinicius Mikuni, postdoctoral researcher in the Data and Analytics Services (DAS) group at Berkeley Lab’s National Energy Research Scientific Computing Center (NERSC) and a NERSC Exascale Science Applications Program for Learning comrade, needed a supercomputer with lots of powerful GPUs (graphics processing units), Nachman said.
Coincidentally, Perlmutter, a new supercomputer designed to support simulation, data analysis and artificial intelligence experiments requiring multiple GPUs at once, had just opened in the summer of 2021 for an “early science phase “, allowing scientists to test the system on real data. (The Perlmutter supercomputer is named after Berkeley Laboratory cosmologist and Nobel laureate Saul Perlmutter.)
“Because the Perlmutter supercomputer allowed us to use 128 GPUs simultaneously, we were able to run all stages of the analysis, from data processing to derivation of results, in less than a week instead of months. This improvement allows us to quickly optimize the neural networks we trained and get a more accurate result for the observables we measured,” said Mikuni, who is also a member of the H1 collaboration.
A central task in these measurements is to take into account the distortions of the detector. The H1 detector, like a vigilant guard at the entrance to a sold-out concert hall, watches for particles as they pass through it. A source of measurement error occurs when particles fly around the detector rather than through it, for example – much like an unticketed concertgoer jumping over an unguarded fence rather than entering through the gate security with ticket.
Simultaneous correction of all distortions had not been possible due to the limited calculation methods available at the time. “Our understanding of subatomic physics and data analysis techniques has advanced significantly since 2007, and so today scientists can use new knowledge to analyze H1 data,” said Nachman.
Scientists now have renewed interest in HERA’s particle experiments, as they hope to use the data – and more accurate computer simulations informed by tools like OmniFold – to help analyze the results of future electron-proton experiments, such as at the Department of Energy’s Next Generation Electron-Ion Collider (EIC). The EIC – to be built at Brookhaven National Laboratory in partnership with the Thomas Jefferson National Accelerator Facility – will be a powerful and versatile new machine capable of colliding high-energy polarized electron beams with a wide range of ions (or charged atoms) through many energies, including polarized protons and some polarized ions.
“It’s exciting to think that our method could one day help scientists answer the lingering questions about the strong force,Nachman said.While this work may not lead to practical applications in the short term, understanding the building blocks of nature is why we are here – to seek the ultimate truth. These are steps to understanding at the most basic level what it’s all about. That’s what motivates me. If we don’t do the research now, we’ll never know what exciting new technological advances we’ll get to benefit future societies.”
In addition to Nachman and Mikuni, members of the Berkeley lab’s H1 team include Fernando Torales Acosta and Yao Xu from the physics division, and Peter Jacobs from the nuclear science division.