They carry a lot of weight. They are at the center of every atom and play an important role in nature.
There's a down-to-earth side to protons as well.
It may sound like science fiction, but flipping a protons' spin is the basis of technology that has become essential to our daily lives.
The inner workings of the protons are a mystery.
We don't understand everything about protons because they exist. Ben Nachman is a physicist who leads the Machine Learning Group in the physics division at the Berkeley Lab.
Understanding how and why protons spin could lead to technological advances we can't even imagine, and help us understand the strong force that gives all protons and therefore atoms mass.
It's difficult to solve. It's not possible to pick up a protons and put it in a petri dish because they are so small. You can't see their insides with the most powerful electron microscopes.
The recent work done by Nachman and his team could help solve the puzzle.
The H1 Collaboration consists of 150 scientists from 50 institutes and 15 countries and is based at the DESY national research center in Germany.
Hera was a giant microscope that accelerated both electrons and protons to the speed of light. The particles hit each other head-on, which scatters a protons into its parts.
The H1 detector was used by scientists at HERA to take measurements of the particle debris cascading from these electron-proton colliders.
Secrets of the strong force are not known.
The H1 stopped gathering data in 2007. The H1 Collaboration is still analyzing the data and publishing the results of their work.
Computational techniques can take a year or more to measure quantities related to the strong force and the amount of particles produced when a protons collides with an electron
If a researcher wanted to look at a different quantity, such as how fast particles are flying in the wake of a quark-gluon jet stream, they would have to start over again.
In order to reduce the amount of time it takes to run an analysis, a new machine learning tool called OmniFold can measure many quantities at the same time.
Neural networks are used to combine simulations with data. Neural network is a machine learning tool that can't be done manually.
For the first time in a June issue of the journal Physical Review Letters, Nachman and his team applied omnifold to H1 experimental data.
The data and analytic services group at Berkeley Lab's National Energy Research Scientific Computing Center is working on a project to test the robustness of OmniFold.
Perlmutter, a new supercomputer designed to support simulation, data analytics, and artificial intelligence experiments requiring multiple GPUs at a time, had just opened up in the summer of 2021. Saul Perlmutter is a Berkeley Lab scientist.
We were able to run all the steps of the analysis in less than a week thanks to the Perlmutter supercomputer. The improvement allows us to quickly improve the neural networks we trained and to achieve a more precise result for the observables we measured.
There is a central task in these measurements. The H1 detector, like a guard at the entrance of a sold-out concert arena, watches particles as they fly through it. One source of measurement errors is when particles fly around the detector rather than through it.
Computational methods at the time did not allow for simultaneous correction of all distortions. Scientists can use new insights to analyze the H1 data thanks to the advanced understanding of subatomic physics and data analysis techniques.
The data from HERA's particle experiments can be used to aid in the analysis of results from future electron-proton experiments, such as at the Department of Energy's next.
The EIC will be a powerful and versatile machine capable of colliding high-energy beams of electrons with a wide range of ion and charged atoms.
One day, scientists could use our method to answer questions about the strong force.
Understanding the building blocks of nature is why we're here, even though this work won't lead to practical applications. At the most basic level, these are steps to understand. That's what makes me want to do something. We will never know what exciting new technological advances will be if we don't do the research now.
More information: V. Andreev et al, Measurement of Lepton-Jet Correlation in Deep-Inelastic Scattering with the H1 Detector Using Machine Learning for Unfolding, Physical Review Letters (2022). DOI: 10.1103/PhysRevLett.128.132002Arxiv.org/abs/1911.09107 is the location of the omnifold.
The conference presentation can be found at www.h1.desy.de/psfiles/confpap.
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