In the domain of particle physics, where scientists are unraveling the secrets of the cosmos, artificial intelligence (AI) and machine learning (ML) are creating waves by improving our understanding of the most fundamental particles. PDFs play an important role in our investigation. These complicated mathematical models are critical for predicting the results of high-energy physics experiments that challenge the Standard Model of particle physics.
PDFs are mathematical models that assist scientists comprehend how protons, particles contained in an atom’s nucleus, function. Protons are composed of even smaller particles known as quarks and gluons, which are together referred to as partons. PDFs show how partons are dispersed within a proton, thereby offering a map of where these small particles are likely to be located and how much momentum they carry.
Scientists may use this knowledge to forecast the results of high energy physics experiments, such those carried out at the Large Hadron Collider, which smashes protons together to investigate basic forces and particles.
The intricacy of these functions and the scarcity of experimental data make modeling them challenging. However, by analyzing vast amounts of data collected by collider facilities, AI and ML provide new methods for analyzing and comprehending these intricate processes.
Theoretical physicists Tim Hobbs and Brandon Kriesten at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are leading the way in applying AI/ML to address the difficulties of modeling PDFs, enhancing the interpretability of the ML models used to do so as well as the accuracy of the PDFs generated from data. This makes it easier for researchers to find patterns, connections, and fundamental ideas in PDFs and the methods used to extract them, which results in more accurate and knowledgeable findings.
Hobbs clarified that elementary or basic particles are the subject of particle physics. Finding flaws in the Standard Model, which was finished in the 1970s, is the present emphasis. Despite its power, we are aware of its limitations because of cosmological clues like dark matter.
PDFdecoder: Bridging Theory and Data
Hobbs and Kriesten recently published a research in Physical Review D that established the “PDFdecoder” concept. It employs encoder-decoder models, which are forms of neural network architecture. These models reduce complicated data to a more understandable format and then recreate the original data from this reduced version.
PDF reconstruction is important because it helps scientists to anticipate particle behavior in high-energy physics experiments. The essential aspects of a PDF are recorded by “Mellin moments,” which are mathematical formulas that describe the distribution of the particles.
According to Kriesten, the model employs generative AI to fill in gaps and replicate initial conditions. In this sense, “initial conditions” refers to the beginning characteristics or configurations required to properly represent the distribution of quarks and gluons in protons.
They investigated how PDFs may be decrypted from Mellin moments and considered several techniques, he added.
This method improves the accuracy of particle physics predictions by ensuring that reconstructed PDFs closely resemble real-world data. It can improve PDF models’ precision, particularly in lattice gauge calculations—a computing. approach that digs into the subtleties of quantum chromodynamics, the theory that describes the strong force binding quarks and gluons.
The PDFdecoder framework introduces Mellin moment data as a novel technique to include lattice information into PDF investigations, therefore enhancing the link between theoretical models and experimental results.
Understanding AI decision-making in theoretical models
Hobbs and Kriesten released another paper in the Journal of High Energy Physics, revealing a new framework named “XAI4PDF.” This framework employs explainable AI strategies, which are ways for making AI models’ decision-making processes more clear and understandable.
The XAI4PDF framework employs ResNet topologies, a form of neural network that exploits shortcuts to increase training efficiency. These shortcuts enable the network to skip particular levels, making it easier to train deep networks without losing essential data.
This paradigm categorizes PDFs based on their fundamental theoretical assumptions. It not only evaluates which theoretical model is most suited to a particular set of PDFs, but it also tracks how various assumptions influence their behavior. This gives useful insights into the elements that drive AI judgments, allowing researchers to better understand the impact of various theoretical parameters.
By adopting techniques initially developed for picture identification, the researchers constructed a powerful tool for studying complicated theoretical models in particle physics.
According to Kriesten, the team utilized computer vision technologies. This enables them to comprehend how various theoretical presumptions alter the characteristics of PDFs.
These frameworks collectively mark a substantial advancement in the use of AI/ML in particle theory.
Evolving high energy physics for the future
According to Hobbs, their work focuses on applying AI and ML to solve challenging particle physics issues. They are opening the door to more accurate predictions in high energy physics experiments by improving the knowledge and precision of PDFs.
AI’s contribution to particle physics is anticipated to grow as it develops, possibly unlocking additional mysteries about the universe. Hobbs and Kriesten are hopeful about the revolutionary potential of AI/ML in theoretical physics. They want to investigate foundation models and broaden their frameworks to include a greater variety of particle interactions in order to adequately represent the intricacy of particle physics.
In addition to improving high energy physics, they are laying the groundwork for future discoveries that have the potential to completely alter our view of the universe by pushing the limits of AI/ML applications.
Jonathan Gomprecht from the University of Arizona is one of the other contributors to this study.
