Articles Citing

Deep learning exotic hadrons

L. Ng^1,*, Ł. Bibrzycki^2,†, J. Nys^3,‡, C. Fernández-Ramírez ^4,5,§, A. Pilloni^6,7,8,∥, V. Mathieu^9,10, A. J. Rasmusson¹¹, and A. P. Szczepaniak^11,12,13 (Joint Physics Analysis Center)

¹Department of Physics, Florida State University, Tallahassee, Florida 32306, USA
²Pedagogical University of Krakow, 30-084 Kraków, Poland
³Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
⁴Departamento de Física Interdisciplinar, Universidad Nacional de Educación a Distancia (UNED), Madrid E-28040, Spain
⁵Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
⁶Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, Messina I-98166, Italy
⁷INFN Sezione di Catania, Catania I-95123, Italy
⁸INFN Sezione di Roma, Roma I-00185, Italy
⁹Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos, Universitat de Barcelona, Martí i Franquès 1, E08028, Spain
¹⁰Departamento de Física Teórica, Universidad Complutense de Madrid and IPARCOS, E-28040 Madrid, Spain
¹¹Department of Physics, Indiana University, Bloomington, Indiana 47405, USA
¹²Theory Center, Thomas Jefferson National Accelerator Facility, Newport News, Virginia 23606, USA
¹³Center for Exploration of Energy and Matter, Indiana University, Bloomington, Indiana 47403, USA

^*ln16@my.fsu.edu
^†lukasz.bibrzycki@up.krakow.pl
^‡jannes.nys@epfl.ch
^§cesar@jlab.org
^∥alessandro.pilloni@unime.it

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Articles Citing

Deep learning exotic hadrons

Dynamical coupled-channel models for hadron dynamics

Deep learning topological inference-guided Tcc+ pole parameter extraction

Deep neural network driven simulation based inference method for pole position estimation under model misspecification

Learning pole structures of hadronic states using predictive uncertainty estimation

Understanding the $$T_{c\bar{c}1}(3900)$$ through Machine Learning Techniques

A survey on generative AI for detector effects unfolding in particle and nuclear physics

No title available

Physics-driven learning for inverse problems in quantum chromodynamics

Pole structure of P ψ N ( 4312 ) + via machine learning and uniformized S-matrix

Exploring machine learning models for predicting meson mass and width

Meson mass and width: Deep learning approach

Deep learning framework for disentangling triangle singularity and pole-based enhancements

Reconstructing S-matrix Phases with Machine Learning

Machine Learning Methods Used in the Automatic System for Teaching Human Motions—Key Aspects of CNN, HMM, and Minimum Distance Algorithms

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

Revealing the nature of hidden charm pentaquarks with machine learning

Strongly Bound Dibaryon with Maximal Beauty Flavor from Lattice QCD

Interpretation of near-threshold peaks using the method of independent S -matrix poles

High-energy nuclear physics meets machine learning

Novel approaches in hadron spectroscopy

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