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...
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...
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...
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.
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.
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....
I will present recent progress on extracting the scattering amplitude for elastic pion-pion scattering from cross-section pseudodata using generative models.
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...
In this talk, I will discuss some new technical developments build a differentiable pipeline for event level analysis of hadron structure.
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.
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...
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...
