AI for Hadron Spectroscopy at JLab

Session

AI for Hadron Spectroscopy at JLab

Jun 4, 2025, 10:30 AM

CC F224-225 & Zoom (Jefferson Lab)

4. Summary talk of NPTwins 2024

Yaohang Li (Old Dominion University)

6/4/25, 10:30 AM

Modern nuclear and high energy physics facilities, including CERN, Jefferson Lab, RHIC, and the upcoming Electron-Ion Collider (EIC), are generating exascale of data. This unprecedented amount of data offers an opportunity to answer many fundamental questions in elementary particle interactions, such as QCD in the nonperturbative regime. The NPTwins2024 workshop, held in Genova, Italy in...

11. EAIRA: Evaluation of LLMs/RLMs as Research Assistants

Franck Cappello (Argonne National Laboratory)

6/4/25, 11:00 AM

Recent advancements have positioned Large Language Models (LLMs) as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants, but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess...

12. Extracting $x$-Dependent GPDs with Normalizing Flow

Zhite Yu (Jefferson Lab)

6/4/25, 11:30 AM

Obtaining the $x$-dependent generalized parton distributions (GPDs) is essential for advancing our understanding of hadron tomography. However, this goal has been hindered by the limited sensitivity of most well-known experimental processes, such as deeply virtual Compton scattering (DVCS) and time-like Compton scattering (TCS). In this talk, I will compare these traditional processes with new...

13. AI Applications in Hadronic Physics

Geoffrey Fox (University of Virginia)

6/4/25, 2:00 PM

We consider the task of using AI for hadron spectroscopy using partial wave analysis combined with production models. There are new challenges not seen in similar tasks at the LHC coming from the parameterization of amplitudes and not cross sections directly. We also have the opportunity and challenge of combining data from the full reaction with reactions with one or more, and even all...

14. Hadron spectroscopy analyses with CLAS and CLAS12

Raffaella De Vita (Jefferson Lab)

6/4/25, 2:30 PM

In this talk, selected spectroscopy analyses completed with CLAS data and now in progress with CLAS12 will be presented, and the challenges that could benefit from AI/ML techniques will be discussed.

17. AI for Unfolding

Benjamin Nachman (LBNL)

6/4/25, 3:00 PM

AI has enabled high-dimensional and unbinned differential cross section measurements for the first time. In this talk, I will discuss state-of-the-art methods and the latest experimental results using these tools.

15. Amplitude-based analyses at GlueX

Mr Boris Grube (Jefferson Lab)

6/4/25, 3:30 PM

The GlueX experiment at Jefferson Lab employs 9 GeV linearly polarized
photons striking a proton target to study the spectrum of light hadrons.
A key focus is the precise measurement of the light-meson spectrum and
the search for exotic mesons. Most spectroscopy analyses rely on
amplitude analysis and require detailed reaction models to accurately
describe the often high-dimensional data....

2. Amplitude extraction with generative AI

Gloria Montana (Jefferson Lab)

6/5/25, 9:00 AM

I will present recent progress on extracting the scattering amplitude for elastic pion-pion scattering from cross-section pseudodata using generative models.

5. Generative-AI for smearing and acceptance unfolding in hadron physics

Tareq Alghamdi (ODU)

6/5/25, 9:30 AM

In particle scattering experiments, detector effects such as smearing and acceptance, distort the measured data, making it challenging to recover the true underlying physics. In this work, we propose an AI-assisted framework that employs Generative Adversarial Networks (GANs) to mitigate the impact of these distortions, enabling accurate reconstruction of the vertex-level distributions...

8. A Modular Software Stack for A(i)DAPT

Trevor Reed (FIU)

6/5/25, 10:00 AM

A(i)DAPT, or AI for Data Analysis and PreservaTion, is a CLAS group, the goal of which is to re-analyze and improve upon the measurements from past CLAS experiments using machine learning. This project utilizes deep learning techniques, specifically generative adversarial networks (GANs), to improve Monte Carlo methods used in the analysis of data. Producing simulations with generative AI as...

10. Diffusion Models as Surrogate Event Generators for QCD Analysis

Jitao Xu

6/5/25, 11:00 AM

Diffusion-based generative models have recently emerged as a powerful alternative to GANs, VAEs, and normalizing flows for learning complex, high-dimensional physics distributions. After briefly introducing the forward–reverse noising process, we demonstrate how a conditional diffusion model can replace the costly Monte-Carlo event generator that maps quantum-correlation-function parameters to...

6. Moments of two-pion photoproduction and diffusion models

Giorgio Foti (University of Messina & INFN Catania), Lukasz Bibrzycki (AGH University of Krakow)

6/5/25, 11:30 AM

In 2009, the CLAS collaboration reported the first observation of scalar meson photoproduction in the π+π− channel. Because the cross section in this channel is dominated by the vector ρ(770) resonance, the observation of the $f_0(980)$ peak in the mass distribution was not possible. Instead, the resonant S-wave contribution was inferred through subtle interference effects in the moments of...

9. Autoencoders for Partial Wave Analysis

Trevor Reed (FIU)

6/5/25, 12:00 PM

Partial Wave Analysis (PWA) provides us with a richer understanding of particle scattering phenomena than, for example, cross sections. PWA has been employed for decades in nuclear physics data, but there are several considerable hurdles that make this topic a challenging one, including large parameter spaces and multiple solutions. In this talk, I will present some of the work done within...

1. Towards simulation based inference of hadron structure

Nobuo Sato (Jefferson Lab)

6/5/25, 2:00 PM

In this talk, I will discuss some new technical developments build a differentiable pipeline for event level analysis of hadron structure.

7. Neural Networks for QCD

Dr Wyatt Smith (University of Messina)

6/5/25, 2:30 PM

In this talk we discuss novel methods for the application of AI to assist with the extraction of physical information from QCD. We discuss neural network architecture and machine learning for the extraction of topological quantities in lattice QCD, and for neural network-enforced unitarity and error propagation in the description of pion scattering data.

16. Variational Neural Network Approach to QFT in the Field Basis: Results from Klein-Gordon Theory

Kevin Braga (William & Mary)

6/5/25, 3:00 PM

We present a variational method for solving quantum field theories in the continuum field basis using neural networks. As a benchmark, we consider the free Klein–Gordon model in one spatial dimension, where the ground-state wavefunctional is known analytically. The variational ansatz is implemented using a feed-forward neural network trained to minimize the Hamiltonian expectation value in the...

3. Applications of Density Ratio Estimation in Experimental Hadron Spectroscopy

Richard Tyson (Jefferson Lab)

6/5/25, 3:30 PM

The solution to many problems can be described by the ratio of the probability densities of two event samples. For example, detector acceptances can be modeled by the ratio of the probability density for detected (accepted) events over that for all events. Similarly, sWeights can be converted to positive definite probabilities via density ratio estimation in order to create machine learning...