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Probing the frontiers of nuclear physics with AI at the EIC (II)

Mar 19 – 21, 2025
America/New_York timezone

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Contribution List

31 out of 31 displayed
  1. 10. Machine Learning Neutrino-Nucleus Cross Sections
    Daniel Hackett (Fermilab)
    3/19/25, 9:00 AM
  2. 11. AI for data preservation and interpretation in hadron physics
    marco battaglieri (INFN-GE)
    3/19/25, 9:35 AM
  3. 12. Quantum states from normalizing flows
    Yukari Yamauchi (Institute for Nuclear Theory, University of Washington)
    3/19/25, 10:40 AM
  4. 13. Quantum dynamics of entanglement and hadronization in jet production in the massive Schwinger model
    David Frenklakh (Brookhaven National Laboratory)
    3/19/25, 11:15 AM
  5. 14. Charged lepton flavor violation searches at the EIC
    Kaori Fuyuto (LANL)
    3/19/25, 1:30 PM
  6. 15. Quasifragmentation functions and quasiparton distributions in the massive Schwinger model
    Sebastian Grieninger (Stony Brook University)
    3/19/25, 2:05 PM
  7. 16. Neural Compression for sPHENIX TPC Data
    Yi Huang (Brookhaven National Lab)
    3/19/25, 3:10 PM
  8. 17. Studying the nucleon tomograhy with GPDs in the future EIC
    Yuxun Guo (Lawrence Berkeley National Laboratory)
    3/19/25, 3:45 PM
  9. 18. Combining forward-LHC and EIC data to search for non-linear QCD evolution
    Peter Jacobs (Lawrence Berkeley National Laboratory)
    3/20/25, 9:00 AM
  10. 29. Tools for unbinned unfolding and an application for jet measurements
    Ryan Milton (UC Riverside)
    3/20/25, 9:35 AM
  11. 20. QCD Theory meets Information Theory
    Benoit Assi (U. Cincinnati)
    3/20/25, 10:40 AM
  12. 21. Foundation models and agents for physics
    Nesar Ramachandra (Argonne National Laboratory)
    3/20/25, 11:15 AM
  13. 22. Generative AI for Jet Analysis and Full Detector Simulation
    Yeonju Go
    3/20/25, 1:30 PM
  14. 23. From Uncertainty to Discovery: Machine Learning at the Frontier of Phenomenology
    Brandon Kriesten (Argonne National Lab)
    3/20/25, 2:05 PM
  15. 24. Preparing the Jet-AI/ML landscape for the EIC
    Rithya Kunnawalkam Elayavalli (Vanderbilt University)
    3/20/25, 3:10 PM
  16. 25. Lattice Gauge Theory Simulations with Gauge Symmetric Neural Network Wave Function
    Di Luo (University of California, Los Angeles)
    3/20/25, 3:45 PM
  17. 28. AI-assisted detector clustering, design, and simulation
    Gregory Matousek (Duke)
    3/20/25, 4:20 PM
  18. 31. Machine Learning Applications for Improving Accelerator Operations at the EIC
    Weijian Lin (Brookhaven National Laboratory)
    3/21/25, 9:00 AM
  19. 27. Gauge theory dynamics with tensor networks
    Navya Gupta (University of Maryland, College Park)
    3/21/25, 9:35 AM
  20. 19. A Wilsonian RG framework for Regression Tasks in Machine Learning
    Anindita Maiti (Perimeter Institute for Theoretical Physics)
    3/21/25, 10:40 AM
  21. 30. Topological Phases and Quantum Hamiltonian Simulations of Wilson Fermions Coupled to U(1) Gauge Fields
    Sriram Sekhar Bharadwaj (UCLA)
    3/21/25, 11:15 AM
  22. 9. AI-assisted detector clustering, design, and simulation
    Gregory Matousek (Duke University)

    We present the progress of detector-focused AI studies at CLAS12 and at the future EIC 2nd detector. At CLAS12, we developed an attention-based model that clusters hits in the experiment’s hodoscopic forward calorimeter. In this model, each hit is treated as a token, with positional encodings learned via a dedicated module incorporating several GravNet layers. Utilizing the object...

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  23. 8. From Uncertainty to Discovery: Machine Learning at the Frontier of Phenomenology
    Brandon Kriesten (Argonne National Lab)

    Uncertainty quantification (UQ) plays a crucial role in the predictive power of nonperturbative quantum correlation functions at high precision. My research explores novel approaches to UQ in the context of parton distribution functions (PDFs), using machine learning techniques to map observables to underlying theoretical models and to navigate the complex parametric landscape of...

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  24. 1. Generative AI for Jet Analysis and Full Detector Simulation
    Yeonju Go (Brookhaven National Laboratory)

    We present denoising diffusion probabilistic models (DDPMs) as high-fidelity, AI-based generative surrogates for producing full-detector, whole-event simulations in heavy-ion experiments [1]. Trained on HIJING minimum-bias data propagated through the sPHENIX detector geometry with Geant4, DDPMs achieve roughly a hundredfold speedup over standard Geant4 simulations and exhibit superior fidelity...

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  25. 3. Machine Learning Applications for Improving Accelerator Operations at the EIC
    Weijian Lin (Brookhaven National Laboratory)

    Particle accelerators are among the largest and most complicated instruments used for basic research in experimental physics. As a result, accelerator operation is always time consuming, not very fault tolerant, and requires expert knowledge. In this talk we will describe how we use machine learning techniques to streamline and improve accelerator operations at multiple accelerators at BNL,...

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  26. 2. Machine Learning for EIC physics
    Sebastian Grieninger (Stony Brook University)

    TBA

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  27. 5. QCD Theory meets Information Theory
    Benoit Assi (U. Cincinnati)

    I will present a novel technique to incorporate precision calculations from quantum chromodynamics into fully differential particle-level Monte-Carlo simulations. By minimizing an information-theoretic quantity subject to constraints, we achieve consistency with the theory input and its estimated systematic uncertainties. Our method can be applied to arbitrary observables known from precision...

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  28. 7. QCD Theory meets Information Theory
    Benoit Assi (U. Cincinnati)

    I will present a novel technique to incorporate precision calculations from quantum chromodynamics into fully differential particle-level Monte-Carlo simulations. By minimizing an information-theoretic quantity subject to constraints, we achieve consistency with the theory input and its estimated systematic uncertainties. Our method can be applied to arbitrary observables known from precision...

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  29. 4. Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence
    Gagik Gavalian (Jefferson Lab)

    Charged track reconstruction is a pivotal task in nuclear physics experiments, enabling the detection and analysis of particles generated in high-energy collisions. Machine learning (ML) has proven to be a transformative tool in this domain, overcoming challenges such as intricate detector geometries, high event multiplicities, and noisy data. While traditional methods like the Kalman filter...

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  30. 6. Studying the nucleon tomograhy with GPDs in the future EIC
    Yuxun Guo (Lawrence Berkeley National Laboratory)

    In this talk, I will discuss some recently progresses in extracting the higher dimensional structures of the nucleon via the framework of generalized parton distributions. Specifically, I will discuss how the proton structure can be studied with the higher-energy exclusive productions of $J/\psi$ particle off the nucleon. In particular, I will discuss the challenge of extracting these...

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  31. 26. TBD
    Ian Cloet (Argonne National Laboratory)