In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experiments, we employ a supervised learning approach using Femtoscopy data. The deep neural networks (DNNs) are trained to learn the inverse mapping from observations to potentials. To link between experiments and first-principles simulations, we further investigate hadronic interactions in Lattice QCD simulations from the HAL QCD method perspective. Using an unsupervised learning approach, we construct a model-free potential function with symmetric DNNs, aiming to learn hadron interactions directly from simulated correlation functions (equal-time Nambu-Bethe-Salpeter amplitudes). On both fronts, deep learning methods show great promise in advancing our understanding of hadron interactions.
@article{arxiv.2501.00374,
title = {Deep learning for exploring hadron-hadron interactions},
author = {Lingxiao Wang},
journal= {arXiv preprint arXiv:2501.00374},
year = {2025}
}
Comments
6 pages, 6 figures, contribution to the EMMI Workshop at the University of Wroclaw: Aspects of Criticality II. arXiv admin note: text overlap with arXiv:2410.03082