6 papers
S-matrix informed neural networks for amplitude analysis
Wyatt A. Smith, Arkaitz Rodas, Marius D. Thomas +5
Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactions relevant to particle physic…
Neuro-dispersive extractions of light-meson resonances
Wyatt A. Smith, Arkaitz Rodas, Marius D. Thomas +5
We present the first dispersive extraction of resonant poles from analytically continued neural networks. We use S-matrix informed neural networks (SINNs) trained to respect unitar…
First steps towards gauge-independent vortex identification through machine learning
Wyatt A. Smith, César Fernández-Ramírez, Jeff Greensite +1
As a first step towards machine identification of confining objects in thermalized lattice gauge configurations, we present our 2dVoId model for center vortex identification on pur…
Finite-volume analysis of the -dibaryon including left-hand-cut effects
Arkaitz Rodas, Lin Qiu, César Fernández-Ramírez +4
We implement the finite-volume representation to study two-baryon interactions from lattice QCD data. We include the left-hand cut induced by one-pion exchange in this formal…
ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
Zeyu Xia, Tyler Kim, Trevor Reed +3
High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential f…
On Dispersive and Nondispersive K-matrix Formalisms
Nils Hüsken, Eric S. Swanson, Adam Szczepaniak
The modeling of coupled-channel effects has become increasingly important due to the availability of highly precise data for a large variety of hadronic (re)scattering processes. T…