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This report is an outcome of the workshop "AI for Nuclear Physics" held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020.
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J. W. T. Keeble and A. Rios, “Machine learning the deuteron,” (2019) , arXiv:1911.13092 [nucl-th]
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Cristiano Fanelli and Jary Pomponi, “DeepRICH: Learning Deeply Cherenkov Detectors,” Sci. Technol. 1
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1911
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1912
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S. Gazula, J.W. Clark, and H. Bohr, “Learning and prediction of nuclear stability by neural networks,” Nucl. Phys. A 540
1992
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K.A. Gernoth, J.W. Clark, J.S. Prater, and H. Bohr, “Neural network models of nuclear systematics,” Phys. Lett. B 300
1993
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J. Corbett, F. Fong, M. Lee, and V. Ziemann, “Optimum steering of photon beam lines in spear,” Proceedings of the 1993 IEEE Particle Accelerator Conference, Washington , 1483–1485 (1993)
1993
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S. Lidia and R. Carr, “Faster magnet sorting with a threshold acceptance algorithm,” Review of Scientific Instrumentation 66
1995
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S. A. Bass, A. Bischoff, J. A. Maruhn, Horst Stoecker, and W. Greiner, “Neural networks for impact parameter determination,” Phys. Rev. C 53
1996
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2001
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2001
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2002
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2002
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2002
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2003
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2003
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2004
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I.V. Bazarov and C.K. Sinclair, “Multivariant optimization of a high brightness dc gun photoinjector,” Physical Review Special Topics Accelerator and Beams 8
2005
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O. Chubar, O. Rudenko, C. Benabderrahmane, O. Marcouille, J. M. Filhol, and M. E. Couprie, “Application of genetic algorithms to sorting, swapping and shimming of the soleil undulator magnets,” AIP Conference Proceedings 879
2007
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2008
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2008
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2011
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W. Gao, L. Wang, and W. Li, “Simultaneous optimization of beam emittance and dynamic aperture for electron storage ring using genetic algorithm,” Physical Review Special Topics Accelerator and Beams 14
2011
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S. Akkoyun, T. Bayram, S. O. Kara, and N. Yildiz, “Consistent empirical physical formulas for potential energy curves of 38–66ti isotopes by using neural networks,” Phys. Part. Nucl. Lett. 10
2013
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A. Hofler, B. Terzic, M. Kramer, A. Zvezdin, V. Morozov, Y. Roblin, F. Lin, and C. Jarvis, “Innovative applications of genetic algorithms to problems in accelerator physics,” Physical Review Special Topics Accelerator and Beams 16
2013
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M. Kortelainen, J. McDonnell, W. Nazarewicz, E. Olsen, P.-G. Reinhard, J. Sarich, N. Schunck, S. M. Wild, D. Davesne, J. Erler, and A. Pastore, “Nuclear energy density optimization: Shell structure,” Phys. Rev. C 89
2014
Cited alongside, same era.
Ani Aprahamian et al. , “Reaching for the horizon: The 2015 long range plan for nuclear science,” (2015)
2015
Cited alongside, same era.
J. D. McDonnell, N. Schunck, D. Higdon, J. Sarich, S. M. Wild, and W. Nazarewicz, “Uncertainty quantification for nuclear density functional theory and information content of new measurements,” Phys. Rev. Lett. 114
2015
Cited alongside, same era.
Z. Liu, Z. He, S.M. Lidia, D. Liu, and Q. Zhao, “Optimization of beam loss monitor network for fault modes,” Proceedings of the 6th International Particle Accelerator Conference, Richmond, VA, May 2015 (2015)
2015
Cited alongside, same era.
“Artificial intelligence for the american people,” (2019)
2019
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“Nuclear physics and quantum information science, a report by the nsac quantum information science subcommittee,” (2019)
2019
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Akira Ohnishi, Yuto Mori, and Kouji Kashiwa, “Path Optimization for the Sign Problem in Field Theories Using Neural Network,” JPS Conf. Proc. 26
2019
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Léo Neufcourt, Yuchen Cao, Witold Nazarewicz, Erik Olsen, and Frederi Viens, “Neutron drip line in the Ca region from Bayesian model averaging,” Phys. Rev. Lett. 122
2019
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2019
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Rajeev S. Bhalerao, Jean-Yves Ollitrault, Subrata Pal, and Derek Teaney, “Principal component analysis of event-by-event fluctuations,” Phys. Rev. Lett. 114
2015
Cited alongside, same era.
Aleksas Mazeliauskas and Derek Teaney, “Subleading harmonic flows in hydrodynamic simulations of heavy ion collisions,” Phys. Rev. C 91
2015
Cited alongside, same era.
Scott Pratt, Evan Sangaline, Paul Sorensen, and Hui Wang, “Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions,” Phys. Rev. Lett. 114
2015
Cited alongside, same era.
“The National Artificial Intelligence Research and Development Strategic Plan, National Science and Technology Council, Networking and Information Technology R&D Subcommittee,” (2016)
2016
Cited alongside, same era.
N. Schunck and L. M. Robledo, “Microscopic theory of nuclear fission: a review,” Rep. Prog. Phys. 79
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M.D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, and P. Vahle, “A convolutional neural network neutrino event classifier,” Journal of Instrumentation 11
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Ubaldo Baños Rodríguez, Cristofher Zuñiga Vargas, Marcello Gonçalves, Sergio Barbosa Duarte, and Fernando Guzmán, “Alpha half-lives calculation of superheavy nuclei with Q α Q\alpha -value predictions based on the Bayesian neural network approach,” J. Phys. G46
2019
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Ubaldo Baños Rodríguez, Cristofher Zuñiga Vargas, Marcello Gonçalves, Sergio Barbosa Duarte, and Fernando Guzmán, “Bayesian Neural Network improvements to nuclear mass formulae and predictions in the SuperHeavy Elements region,” EPL 127
2019
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Zi-Ao Wang, Junchen Pei, Yue Liu, and Yu Qiang, “Bayesian evaluation of incomplete fission yields,” Phys. Rev. Lett. 123
2019
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A. Lovell, A. Mohan, P. Talou, and M. Chertkov, “Constraining fission yields using machine learning,” EPJ Web Conf. 211
2019
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2019
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M. Catacora-Rios, G. B. King, A. E. Lovell, and F. M. Nunes, “Exploring experimental conditions to reduce uncertainties in the optical potential,” Phys. Rev. C100
2019
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W. G. Jiang, G. Hagen, and T. Papenbrock, “Extrapolation of nuclear structure observables with artificial neural networks,” Phys. Rev. C 100
2019
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Gianina Alina Negoita, James P. Vary, Glenn R. Luecke, Pieter Maris, Andrey M. Shirokov, Ik Jae Shin, Youngman Kim, Esmond G. Ng, Chao Yang, Matthew Lockner, and Gurpur M. Prabhu, “Deep learning: Extrapolation tool for ab initio nuclear theory,” Phys. Rev. C 99
2019
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A Ekström, C Forssén, C Dimitrakakis, D Dubhashi, H T Johansson, A S Muhammad, H Salomonsson, and A Schliep, “Bayesian optimization in ab initio nuclear physics,” J. Phys. G 46
2019
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Andreas Ekström and Gaute Hagen, “Global sensitivity analysis of bulk properties of an atomic nucleus,” Phys. Rev. Lett. 123
2019
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C. J. Horowitz et al. , “r-process nucleosynthesis: connecting rare-isotope beam facilities with the cosmos,” J. Phys. G 46
2019
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P. Morfouace, C.Y. Tsang, Y. Zhang, W.G. Lynch, M.B. Tsang, D.D.S. Coupland, M. Youngs, Z. Chajecki, M.A. Famiano, T.K. Ghosh, G. Jhang, Jenny Lee, H. Liu, A. Sanetullaev, R. Showalter, and J. Winkelbauer, “Constraining the symmetry energy with heavy-ion collisions and bayesian analyses,” Phys. Lett. B 799
2019
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C.Y. Tsang, M.B. Tsang, Pawel Danielewicz, F.J. Fattoyev, and W.G. Lynch, “Insights on skyrme parameters from gw170817,” Phys. Lett. B 796
2019
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Y. Lim and J. W. Holt, “Bayesian modeling of the nuclear equation of state for neutron star tidal deformabilities and GW170817,” EPJA 55
2019
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A. Solopova, A. Carpenter, T. Powers, Y. Roblin, C. Tennant, K. Iftekharuddin, and L. Vidyaratne, “Srf cavity fault classification using machine learning at cebaf,” Proceedings of the 2019 International Particle Accelerator Conference, Melbourne, 2019 (2019)
2019
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E. Fol, J.M Coello de Portugal, and R. Tomas, “Unsupervised machine learning for detection of faulty bpms,” Proceedings of the 2019 International Particle Accelerator Conference, Melbourne, 2019 (2019)
2019
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M. P. Kuchera, Raghuram Ramanujan, J. Z. Taylor, R. R. Strauss, D. Bazin, J. Bradt, and Ruiming Chen, “Machine learning methods for track classification in the AT-TPC,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment (2019)
2019
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F. Barbosa et al. , “A new Transition Radiation detector based on GEM technology,” Nucl. Instrum. Meth. A942
2019
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Ziming Liu, Wenbin Zhao, and Huichao Song, “Principal Component Analysis of collective flow in Relativistic Heavy-Ion Collisions,” Eur. Phys. J. C 79
2019
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Jonah E. Bernhard, J. Scott Moreland, and Steffen A. Bass, “Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma,” Nature Phys. 15
2019
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Ron Soltz (Jetscape), “Bayesian extraction of q ^ \hat{q} with multi-stage jet evolution approach,” Proceedings, 9th International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions: Hard Probes 2018 (HP2018): Aix-Les-Bains, France, October 1-5, 2018 , PoS HardProbes2018
2019
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2019
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Jan Steinheimer, Longgang Pang, Kai Zhou, Volker Koch, Jørgen Randrup, and Horst Stoecker, “A machine learning study to identify spinodal clumping in high energy nuclear collisions,” JHEP 12
2019
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A. Scheinker, D. Bohler, S. Tomin, R. Kammering, I. Zagorodnov, H. Schlarb, M. Scholz, B. Beutner, and W. Decking, “Model-independent tuning for maximizing free electron laser pulse energy,” Physical Review Accelerators and Beams 22
2019
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“Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence,” (2019)
2019
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“ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources,” (2019)
2019
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“Data and Models: A Framework for Advancing AI in Science,” (2019)
2019
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“Neurodata,” (2020)
2020
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“Candle project,” (2020)
2020
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Giovani Pederiva and Andrea Shindler, “Machine Learning for Hadron Correlators from lattice QCD,” Work in progress (2020)
2020
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2020
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S. Akkoyun, “Estimation of fusion reaction cross-sections by artificial neural networks,” NIM B 462
2020
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M. Rescic, R. Seviour, and W. Blokland, “Predicting particle accelerator failures using binary classifiers,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 955
2020
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J. F. Paquet et al. (JETSCAPE), “Revisiting Bayesian constraints on the transport coefficients of QCD,” (2020)
2020
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V. Sobes, M. Grosskopf, K. Wendt, D. Brown, M. S. Smith, and P. Talou, “WANDA: AI/ML for nuclear data. summary of the session on AI/ML at the workshop on applied nuclear data activities 2020, March 3-5, 2020, ORNL/TM-2020/1535,”
2020
Closest in time.