Revealing the nature of hidden charm pentaquarks with machine learning.

[1]  L. M. Brown,et al.  Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector. , 2023, Physical review letters.

[2]  Jia-Jun Wu,et al.  Rediscovery of numerical Lüscher's formula from the neural network , 2022, ArXiv.

[3]  Jifeng Hu,et al.  Approach the Gell-Mann-Okubo Formula with Machine Learning , 2022, Chinese Physics Letters.

[4]  Xiaopeng Dong,et al.  Nuclear charge radii in Bayesian neural networks revisited , 2022, Physics Letters B.

[5]  C. Urbach,et al.  Towards a theory of hadron resonances , 2022, Physics Reports.

[6]  Zhenyu Zhang,et al.  Study of exotic hadrons with machine learning , 2022, Physical Review D.

[7]  E. Passemar,et al.  Novel approaches in hadron spectroscopy , 2021, Progress in Particle and Nuclear Physics.

[8]  W. Nazarewicz,et al.  Machine Learning in Nuclear Physics , 2021, 2112.02309.

[9]  U. Meißner,et al.  Three-body renormalization group limit cycles based on unsupervised feature learning , 2021, Mach. Learn. Sci. Technol..

[10]  A. Szczepaniak,et al.  Deep learning exotic hadrons , 2021, Physical Review D.

[11]  I. Belyaev.  Study of the doubly charmed tetraquark $T_{cc}^+$ , 2021 .

[12]  R. A. Mohammed,et al.  Observation of an exotic narrow doubly charmed tetraquark , 2021, Nature Physics.

[13]  R. A. Mohammed,et al.  Study of the doubly charmed tetraquark [Formula: see text]. , 2021, Nature communications.

[14]  R. Kueng,et al.  Provably efficient machine learning for quantum many-body problems , 2021, Science.

[15]  Toru Sato,et al.  Model independent analysis of coupled-channel scattering: A deep learning approach , 2021, Physical Review D.

[16]  Toru Sato,et al.  Unveiling the pole structure of S-matrix using deep learning , 2021, Suplemento de la Revista Mexicana de Física.

[17]  B. Zou.  Building up the spectrum of pentaquark states as hadronic molecules. , 2021, Science bulletin.

[18]  Dean Lee,et al.  A.I. for nuclear physics , 2021, The European Physical Journal A.

[19]  J. A. Oller,et al.  Revisiting the nature of the Pc pentaquarks , 2021, Journal of High Energy Physics.

[20]  A. Lovato,et al.  Variational Monte Carlo Calculations of A≤4 Nuclei with an Artificial Neural-Network Correlator Ansatz. , 2020, Physical review letters.

[21]  R. A. Mohammed,et al.  Observation of structure in the J/ψ-pair mass spectrum. , 2020, Science bulletin.

[22]  J M Clavijo,et al.  Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier , 2020, Mach. Learn. Sci. Technol..

[23]  Toru Sato,et al.  Classifying the pole of an amplitude using a deep neural network , 2020, 2003.10770.

[24]  Bastian Kaspschak,et al.  How machine learning conquers the unitary limit , 2020, Communications in Theoretical Physics.

[25]  Chun-Wang Ma,et al.  Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method , 2020, Chinese Physics C.

[26]  F. Guo,et al.  Threshold cusps and triangle singularities in hadronic reactions , 2019, Progress in Particle and Nuclear Physics.

[27]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[28]  J. Keeble,et al.  Machine learning the deuteron , 2019, Physics Letters B.

[29]  J. A. Oller,et al.  Interpretation of the LHCb P_{c} States as Hadronic Molecules and Hints of a Narrow P_{c}(4380). , 2019, Physical review letters.

[30]  Bo Wang,et al.  Hidden-charm and hidden-bottom molecular pentaquarks in chiral effective field theory , 2019, Journal of High Energy Physics.

[31]  Christopher E. Thomas,et al.  The XYZ states: Experimental and theoretical status and perspectives , 2019, Physics Reports.

[32]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol , 2019, Chemical science.

[33]  F. Bedeschi,et al.  Observation of a Narrow Pentaquark State, P_{c}(4312)^{+}, and of the Two-Peak Structure of the P_{c}(4450)^{+}. , 2019, Physical review letters.

[34]  Yan-Rui Liu,et al.  Pentaquark and Tetraquark States , 2019, Progress in Particle and Nuclear Physics.

[35]  L. Geng,et al.  Emergence of a Complete Heavy-Quark Spin Symmetry Multiplet: Seven Molecular Pentaquarks in Light of the Latest LHCb Analysis. , 2019, Physical review letters.

[36]  F. Navarra,et al.  QCD sum rules approach to the X, Y and Z states , 2018, Journal of Physics G: Nuclear and Particle Physics.

[37]  Z. Niu,et al.  Predictions of nuclear β -decay half-lives with machine learning and their impact on r -process nucleosynthesis , 2018, Physical Review C.

[38]  D. Whiteson,et al.  Deep Learning and Its Application to LHC Physics , 2018, Annual Review of Nuclear and Particle Science.

[39]  Z. Niu,et al.  Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects , 2018, 1801.04411.

[40]  A. Faessler,et al.  Description of heavy exotic resonances as molecular states using phenomenological Lagrangians , 2017 .

[41]  Hadronic molecules , 2017 .

[42]  Eric S. Swanson,et al.  Heavy-quark QCD exotica , 2016, 1610.04528.

[43]  Yan-Rui Liu,et al.  A review of the open charm and open bottom systems , 2016, Reports on progress in physics. Physical Society.

[44]  Matthias Troyer,et al.  Solving the quantum many-body problem with artificial neural networks , 2016, Science.

[45]  I. A. Monroy,et al.  Model-Independent Evidence for J/ψp Contributions to Λ_{b}^{0}→J/ψpK^{-} Decays. , 2016, Physical review letters.

[46]  P. Baldi,et al.  Jet Substructure Classification in High-Energy Physics with Deep Neural Networks , 2016, 1603.09349.

[47]  Hua-Xing Chen,et al.  The hidden-charm pentaquark and tetraquark states , 2016, 1601.02092.

[48]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[50]  J. Latorre,et al.  Parton distributions for the LHC run II , 2014, 1410.8849.

[51]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.

[53]  F. Guo,et al.  Y ( 4260 ) as the first S -wave open charm vector molecular state? , 2013, 1310.2190.

[54]  J. Bain.  Effective field theories , 2013 .

[55]  M. B. Voloshin,et al.  Radiative transitions from Upsilon(5S) to molecular bottomonium , 2011, 1105.5829.

[56]  Xiang Liu,et al.  Possible hidden-charm molecular baryons composed of an anti-charmed meson and a charmed baryon , 2011, 1105.2901.

[57]  Shimon Whiteson,et al.  Machine learning for event selection in high energy physics , 2009, Eng. Appl. Artif. Intell..

[58]  J. M. Udias,et al.  Properties of nucleon resonances by means of a genetic algorithm , 2008, 0805.4178.

[59]  Jan Ryckebusch,et al.  A genetic algorithm analysis of N∗ resonances in p(γ,K+)Λ reactions , 2004 .

[60]  J. Latorre,et al.  Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates , 2004, hep-ph/0401047.

[61]  J. Ryckebusch,et al.  Analysis of N* Resonances in $p(\gamma,K^{+})\Lambda$ Reactions , 2003, nucl-th/0312103.

[62]  Brian Gough,et al.  GNU Scientific Library Reference Manual - Third Edition , 2003 .

[63]  L. Garrido,et al.  Neural network parametrization of deep inelastic structure functions , 2002, hep-ph/0204232.

[64]  Fons Rademakers,et al.  ROOT — An object oriented data analysis framework , 1997 .

[65]  Fionn Murtagh,et al.  Multilayer perceptrons for classification and regression , 1991, Neurocomputing.

[66]  M. Dorigo,et al.  Model-Independent Evidence for J = ψ p Contributions to Λ 0 b → J = ψ pK , 2016 .

[67]  I. A. Monroy,et al.  Observation of J/p resonances consistent with pentaquark states in 0b J/K-p decays , 2015 .

[68]  M. Voloshin.  Radiative transitions from Υ(5S) to molecular bottomonium , 2014 .

[69]  W. Marsden.  I and J , 2012 .

[70]  刘翔,et al.  Possible hidden-charm molecular baryons composed of an anti-charmed meson and a charmed baryon , 2012 .

[71]  M. E. Galassi,et al.  GNU SCIENTI C LIBRARY REFERENCE MANUAL , 2005 .