Colloquium: Machine learning in nuclear physics

Boehnlein, Amber; Diefenthaler, Markus; Sato, Nobuo; Schram, Malachi; Ziegler, Veronique; Fanelli, Cristiano; Hjorth-Jensen, Morten; Horn, Tanja; Kuchera, Michelle P.; Lee, Dean; Nazarewicz, Witold; Ostroumov, Peter; Orginos, Kostas; Poon, Alan; Wang, Xin-Nian; Scheinker, Alexander; Smith, Michael S.; Pang, Long-Gang

doi:10.1103/revmodphys.94.031003

Title: Colloquium: Machine learning in nuclear physics

Journal Article · 08 September 2022 · Reviews of Modern Physics

DOI: https://doi.org/10.1103/revmodphys.94.031003 · OSTI ID:1886246

^[1];

^[1]; Sato, Nobuo ^[1];

^[1]; Ziegler, Veronique ^[1];

^[2];

^[3];

^[4]; Kuchera, Michelle P. ^[5];

^[6];

^[6]; Ostroumov, Peter ^[6]; Orginos, Kostas ^[7];

^[8]; Wang, Xin-Nian ^[8];

^[9];

^[10];

^[11]

Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
University of Oslo (Norway)
The Catholic University of America, Washington, DC (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Davidson College, NC (United States)
Michigan State University, East Lansing, MI (United States)
College of William and Mary, Williamsburg, VA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Central China Normal University, Wuhan (China)

We report advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF); USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: AC05-06OR23177; SC0019999; SC0021152; SC0013365; SC0018083; FG02-04ER41302; AC02-05CH11231; 89233218CNA000001; AC05-00OR22725; PHY-1404159; PHY-2013047; PHY-2012430; PHY-20122865

OSTI ID:: 1886246

Alternate ID(s):: OSTI ID: 1922348; OSTI ID: 1924033

Report Number(s):: JLAB-CST-22-3603; DOE/OR/23177-5478; arXiv:2112.02309; PHY-1404159; PHY-2013047; PHY-2012430; PHY-2012865; 2004601; 20210595DR; 11861131009; 12075098; DE-SC0018083; C2-2020-FEMT-006; C2019-FEMT-002-05; TRN: US2309684

Journal Information:: Reviews of Modern Physics, Vol. 94, Issue 3; ISSN 0034-6861

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

References (325)

Uncertainty quantification of an empirical shell-model interaction using principal component analysis Fox, Jordan M. R.; Johnson, Calvin W.; Perez, Rodrigo Navarro Physical Review C, Vol. 101, Issue 5 https://doi.org/10.1103/PhysRevC.101.054308	journal	May 2020
Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach Utama, R.; Piekarewicz, J.; Prosper, H. B. Physical Review C, Vol. 93, Issue 1 https://doi.org/10.1103/PhysRevC.93.014311	journal	January 2016
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment Kekic, M.; Adams, C.; Woodruff, K. Journal of High Energy Physics, Vol. 2021, Issue 1 https://doi.org/10.1007/JHEP01(2021)189	journal	January 2021
An equation-of-state-meter of quantum chromodynamics transition from deep learning Pang, Long-Gang; Zhou, Kai; Su, Nan Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-017-02726-3	journal	January 2018
Extremum seeking for optimal control problems with unknown time‐varying systems and unknown objective functions Scheinker, Alexander; Scheinker, David International Journal of Adaptive Control and Signal Processing, Vol. 35, Issue 7 https://doi.org/10.1002/acs.3097	journal	February 2020
Quantifying uncertainties in neutron- α scattering with chiral nucleon-nucleon and three-nucleon forces Kravvaris, Konstantinos; Quinlan, Kevin R.; Quaglioni, Sofia Physical Review C, Vol. 102, Issue 2 https://doi.org/10.1103/PhysRevC.102.024616	journal	August 2020
Constraining the Dense Matter Equation of State with Joint Analysis of NICER and LIGO/Virgo Measurements Raaijmakers, G.; Greif, S. K.; Riley, T. E. The Astrophysical Journal, Vol. 893, Issue 1 https://doi.org/10.3847/2041-8213/ab822f	journal	April 2020
Mapping distinct phase transitions to a neural network Bachtis, Dimitrios; Aarts, Gert; Lucini, Biagio Physical Review E, Vol. 102, Issue 5 https://doi.org/10.1103/PhysRevE.102.053306	journal	November 2020
Nuclei with Up to $$\varvec{A=6}$$ Nucleons with Artificial Neural Network Wave Functions Gnech, Alex; Adams, Corey; Brawand, Nicholas Few-Body Systems, Vol. 63, Issue 1 https://doi.org/10.1007/s00601-021-01706-0	journal	December 2021
Electromagnetic showers beyond shower shapes de Oliveira, Luke; Nachman, Benjamin; Paganini, Michela Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 951 https://doi.org/10.1016/j.nima.2019.162879	journal	January 2020
Machine learning of explicit order parameters: From the Ising model to SU(2) lattice gauge theory Wetzel, Sebastian J.; Scherzer, Manuel Physical Review B, Vol. 96, Issue 18 https://doi.org/10.1103/PhysRevB.96.184410	journal	November 2017
Reconstructing the kinematics of deep inelastic scattering with deep learning Arratia, Miguel; Britzger, Daniel; Long, Owen Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1025 https://doi.org/10.1016/j.nima.2021.166164	journal	February 2022
Sampling using SU ( N ) gauge equivariant flows Boyda, Denis; Kanwar, Gurtej; Racanière, Sébastien Physical Review D, Vol. 103, Issue 7 https://doi.org/10.1103/PhysRevD.103.074504	journal	April 2021
Novel particle tracking algorithm based on the Random Sample Consensus Model for the Active Target Time Projection Chamber (AT-TPC) Ayyad, Yassid; Mittig, Wolfgang; Bazin, Daniel Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 880 https://doi.org/10.1016/j.nima.2017.10.090	journal	February 2018
Bayesian Optimization of a Free-Electron Laser Duris, J.; Kennedy, D.; Hanuka, A. Physical Review Letters, Vol. 124, Issue 12 https://doi.org/10.1103/PhysRevLett.124.124801	journal	March 2020
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models Nicoli, Kim A.; Anders, Christopher J.; Funcke, Lena Physical Review Letters, Vol. 126, Issue 3 https://doi.org/10.1103/PhysRevLett.126.032001	journal	January 2021
Constraining the Equation of State of Superhadronic Matter from Heavy-Ion Collisions Pratt, Scott; Sangaline, Evan; Sorensen, Paul Physical Review Letters, Vol. 114, Issue 20 https://doi.org/10.1103/PhysRevLett.114.202301	journal	May 2015
Adaptive method for electron bunch profile prediction Scheinker, Alexander; Gessner, Spencer Physical Review Special Topics - Accelerators and Beams, Vol. 18, Issue 10 https://doi.org/10.1103/PhysRevSTAB.18.102801	journal	October 2015
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks Maevskiy, A.; Derkach, D.; Kazeev, N. Journal of Physics: Conference Series, Vol. 1525, Issue 1 https://doi.org/10.1088/1742-6596/1525/1/012097	journal	April 2020
Pulse shape discrimination for Gerda Phase I data Agostini, M.; Allardt, M.; Andreotti, E. The European Physical Journal C, Vol. 73, Issue 10 https://doi.org/10.1140/epjc/s10052-013-2583-7	journal	October 2013
Search for Majorana Neutrinos Near the Inverted Mass Hierarchy Region with KamLAND-Zen Gando, A.; Gando, Y.; Hachiya, T. Physical Review Letters, Vol. 117, Issue 8 https://doi.org/10.1103/PhysRevLett.117.082503	journal	August 2016
Machine-learning approach to finite-size effects in systems with strongly interacting fermions Ismail, Nawar; Gezerlis, Alexandros Physical Review C, Vol. 104, Issue 5 https://doi.org/10.1103/PhysRevC.104.055802	journal	November 2021
Predictions of nuclear charge radii and physical interpretations based on the naive Bayesian probability classifier Ma, Yunfei; Su, Chen; Liu, Jian Physical Review C, Vol. 101, Issue 1 https://doi.org/10.1103/PhysRevC.101.014304	journal	January 2020
Vertex and energy reconstruction in JUNO with machine learning methods Qian, Zhen; Belavin, Vladislav; Bokov, Vasily Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1010 https://doi.org/10.1016/j.nima.2021.165527	journal	September 2021
Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz Adams, Corey; Carleo, Giuseppe; Lovato, Alessandro Physical Review Letters, Vol. 127, Issue 2 https://doi.org/10.1103/PhysRevLett.127.022502	journal	July 2021
Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method Ma, Chun-Wang; Peng, Dan; Wei, Hui-Ling Chinese Physics C, Vol. 44, Issue 1 https://doi.org/10.1088/1674-1137/44/1/014104	journal	January 2020
The identification of $${{\varvec{\alpha }}}$$-clustered doorway states in $$^{44,48,52}$$Ti using machine learning Bailey, Sam; Kokalova, Tzany; Freer, Martin The European Physical Journal A, Vol. 57, Issue 3 https://doi.org/10.1140/epja/s10050-021-00357-3	journal	March 2021
Accelerating coupled cluster calculations with nonlinear dynamics and supervised machine learning Agarawal, Valay; Roy, Samrendra; Chakraborty, Anish The Journal of Chemical Physics, Vol. 154, Issue 4 https://doi.org/10.1063/5.0037090	journal	January 2021
Identifying heavy-flavor jets using vectors of locally aggregated descriptors Bielčíková, J.; Kunnawalkam Elayavalli, R.; Ponimatkin, G. Journal of Instrumentation, Vol. 16, Issue 03 https://doi.org/10.1088/1748-0221/16/03/P03017	journal	March 2021
Fast & accurate emulation of two-body scattering observables without wave functions Melendez, J. A.; Drischler, C.; Garcia, A. J. Physics Letters B, Vol. 821 https://doi.org/10.1016/j.physletb.2021.136608	journal	October 2021
Deep learning and holographic QCD Hashimoto, Koji; Sugishita, Sotaro; Tanaka, Akinori Physical Review D, Vol. 98, Issue 10 https://doi.org/10.1103/PhysRevD.98.106014	journal	November 2018
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators Li, Sichen; Zacharias, Mélissa; Snuverink, Jochem Information, Vol. 12, Issue 3 https://doi.org/10.3390/info12030121	journal	March 2021
Neural network models of nuclear systematics Gernoth, K. A.; Clark, J. W.; Prater, J. S. Physics Letters B, Vol. 300, Issue 1-2 https://doi.org/10.1016/0370-2693(93)90738-4	journal	February 1993
Topological defects and confinement with machine learning: The case of monopoles in compact electrodynamics Chernodub, M. N.; Erbin, Harold; Goy, V. A. Physical Review D, Vol. 102, Issue 5 https://doi.org/10.1103/PhysRevD.102.054501	journal	September 2020
Statistical Global Modeling of β−-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines Costiris, N.; Mavrommatis, E.; Gernoth, K. A. HNPS Proceedings, Vol. 16 https://doi.org/10.12681/hnps.2602	journal	January 2020
Probing Majorana neutrinos with double-β decay Agostini, M.; Bakalyarov, A. M.; Balata, M. Science, Vol. 365, Issue 6460 https://doi.org/10.1126/science.aav8613	journal	September 2019
A first unbiased global NLO determination of parton distributions and their uncertainties Ball, Richard D.; Del Debbio, Luigi; Forte, Stefano Nuclear Physics B, Vol. 838, Issue 1-2 https://doi.org/10.1016/j.nuclphysb.2010.05.008	journal	October 2010
Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions Del Debbio, Luigi; Giani, Tommaso; Karpie, Joseph Journal of High Energy Physics, Vol. 2021, Issue 2 https://doi.org/10.1007/JHEP02(2021)138	journal	February 2021
The Elements of Statistical Learning Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome Springer Series in Statistics https://doi.org/10.1007/978-0-387-84858-7	book	January 2009
Policy gradient methods for free-electron laser and terahertz source optimization and stabilization at the FERMI free-electron laser at Elettra O’Shea, F. H.; Bruchon, N.; Gaio, G. Physical Review Accelerators and Beams, Vol. 23, Issue 12 https://doi.org/10.1103/PhysRevAccelBeams.23.122802	journal	December 2020
Covariant approximation averaging Shintani, Eigo; Arthur, Rudy; Blum, Thomas Physical Review D, Vol. 91, Issue 11 https://doi.org/10.1103/PhysRevD.91.114511	journal	June 2015
Constraining the Equation of State of High-density Cold Matter Using Nuclear and Astronomical Measurements Miller, M. C.; Chirenti, C.; Lamb, F. K. The Astrophysical Journal, Vol. 888, Issue 1 https://doi.org/10.3847/1538-4357/ab4ef9	journal	December 2019
Path Optimization for the Sign Problem in Field Theories Using Neural Network Ohnishi, Akira; Mori, Yuto; Kashiwa, Kouji Proceedings of the 8th International Conference on Quarks and Nuclear Physics (QNP2018) https://doi.org/10.7566/JPSCP.26.024011	conference	November 2019
Multimessenger and multiphysics Bayesian inference for the GW170817 binary neutron star merger Güven, H.; Bozkurt, K.; Khan, E. Physical Review C, Vol. 102, Issue 1 https://doi.org/10.1103/PhysRevC.102.015805	journal	July 2020
Improving predictive inference under covariate shift by weighting the log-likelihood function Shimodaira, Hidetoshi Journal of Statistical Planning and Inference, Vol. 90, Issue 2 https://doi.org/10.1016/S0378-3758(00)00115-4	journal	October 2000
Deep learning beyond Lefschetz thimbles Alexandru, Andrei; Bedaque, Paulo F.; Lamm, Henry Physical Review D, Vol. 96, Issue 9 https://doi.org/10.1103/PhysRevD.96.094505	journal	November 2017
Pileup Mitigation with Machine Learning (PUMML) Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin Journal of High Energy Physics, Vol. 2017, Issue 12 https://doi.org/10.1007/JHEP12(2017)051	journal	December 2017
Quantifying correlated truncation errors in effective field theory Melendez, J. A.; Furnstahl, R. J.; Phillips, D. R. Physical Review C, Vol. 100, Issue 4 https://doi.org/10.1103/PhysRevC.100.044001	journal	October 2019
Neutron star mass and radius measurements from atmospheric model fits to X-ray burst cooling tail spectra Nättilä, J.; Miller, M. C.; Steiner, A. W. Astronomy & Astrophysics, Vol. 608 https://doi.org/10.1051/0004-6361/201731082	journal	December 2017
On Nonintrusive Uncertainty Quantification and Surrogate Model Construction in Particle Accelerator Modeling Adelmann, Andreas SIAM/ASA Journal on Uncertainty Quantification, Vol. 7, Issue 2 https://doi.org/10.1137/16M1061928	journal	January 2019
Covariant Density Functional Theory in Nuclear Physics and Astrophysics Yang, Junjie; Piekarewicz, J. Annual Review of Nuclear and Particle Science, Vol. 70, Issue 1 https://doi.org/10.1146/annurev-nucl-101918-023608	journal	October 2020
Final Results of GERDA on the Search for Neutrinoless Double- β Decay Agostini, M.; Araujo, G. R.; Bakalyarov, A. M. Physical Review Letters, Vol. 125, Issue 25 https://doi.org/10.1103/PhysRevLett.125.252502	journal	December 2020
Machine learning estimators for lattice QCD observables Yoon, Boram; Bhattacharya, Tanmoy; Gupta, Rajan Physical Review D, Vol. 100, Issue 1 https://doi.org/10.1103/PhysRevD.100.014504	journal	July 2019
Machine-learning prediction for quasiparton distribution function matrix elements Zhang, Rui; Fan, Zhouyou; Li, Ruizi Physical Review D, Vol. 101, Issue 3 https://doi.org/10.1103/PhysRevD.101.034516	journal	February 2020
Bayesian approach to model-based extrapolation of nuclear observables Neufcourt, Léo; Cao, Yuchen; Nazarewicz, Witold Physical Review C, Vol. 98, Issue 3 https://doi.org/10.1103/PhysRevC.98.034318	journal	September 2018
Validating neural-network refinements of nuclear mass models Utama, R.; Piekarewicz, J. Physical Review C, Vol. 97, Issue 1 https://doi.org/10.1103/PhysRevC.97.014306	journal	January 2018
The description of giant dipole resonance key parameters with multitask neural networks Bai, J. H.; Niu, Z. M.; Sun, B. Y. Physics Letters B, Vol. 815 https://doi.org/10.1016/j.physletb.2021.136147	journal	April 2021
Designing optimal experiments: an application to proton Compton scattering Melendez, J. A.; Furnstahl, R. J.; Grießhammer, H. W. The European Physical Journal A, Vol. 57, Issue 3 https://doi.org/10.1140/epja/s10050-021-00382-2	journal	February 2021
A Bayesian analysis of nuclear deformation properties with Skyrme energy functionals Schunck, N.; Quinlan, K. R.; Bernstein, J. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 10 https://doi.org/10.1088/1361-6471/aba4fa	journal	September 2020
Nuclear masses in extended kernel ridge regression with odd-even effects Wu, X. H.; Guo, L. H.; Zhao, P. W. Physics Letters B, Vol. 819 https://doi.org/10.1016/j.physletb.2021.136387	journal	August 2021
A Bayesian approach for parameter estimation and prediction using a computationally intensive model Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3 https://doi.org/10.1088/0954-3899/42/3/034009	journal	February 2015
Searching for Neutrinoless Double-Beta Decay of ¹³⁰ Te with CUORE Artusa, D. R.; Avignone, F. T.; Azzolini, O. Advances in High Energy Physics, Vol. 2015 https://doi.org/10.1155/2015/879871	journal	January 2015
Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering Dalitz, Christoph; Ayyad, Yassid; Wilberg, Jens Computer Physics Communications, Vol. 235 https://doi.org/10.1016/j.cpc.2018.09.010	journal	February 2019
Parton distribution functions from lattice QCD using Bayes-Gauss-Fourier transforms Alexandrou, Constantia; Iannelli, Giovanni; Jansen, Karl Physical Review D, Vol. 102, Issue 9 https://doi.org/10.1103/PhysRevD.102.094508	journal	November 2020
Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav Chemistry of Materials, Vol. 22, Issue 12 https://doi.org/10.1021/cm100795d	journal	June 2010
Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron Yüksel, Esra; Soydaner, Derya; Bahtiyar, Hüseyin International Journal of Modern Physics E, Vol. 30, Issue 03 https://doi.org/10.1142/S0218301321500178	journal	March 2021
Constraining Fission Yields Using Machine Learning Lovell, Amy; Mohan, Arvind; Talou, Patrick EPJ Web of Conferences, Vol. 211 https://doi.org/10.1051/epjconf/201921104006	journal	January 2019
Machine learning-based longitudinal phase space prediction of particle accelerators Emma, C.; Edelen, A.; Hogan, M. J. Physical Review Accelerators and Beams, Vol. 21, Issue 11 https://doi.org/10.1103/PhysRevAccelBeams.21.112802	journal	November 2018
Exploring Bayesian parameter estimation for chiral effective field theory using nucleon–nucleon phase shifts Wesolowski, S.; Furnstahl, R. J.; Melendez, J. A. Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 4 https://doi.org/10.1088/1361-6471/aaf5fc	journal	February 2019
Completing density functional theory by machine learning hidden messages from molecules Nagai, Ryo; Akashi, Ryosuke; Sugino, Osamu npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0310-0	journal	May 2020
Machine learning to alleviate Hubbard-model sign problems Wynen, Jan-Lukas; Berkowitz, Evan; Krieg, Stefan Physical Review B, Vol. 103, Issue 12 https://doi.org/10.1103/PhysRevB.103.125153	journal	March 2021
Fabrication, characterization and analysis of a prototype high purity germanium detector for $$^{76}$$Ge-based neutrinoless double beta decay experiments Jany, A.; Misiaszek, M.; Mroz, T. The European Physical Journal C, Vol. 81, Issue 1 https://doi.org/10.1140/epjc/s10052-020-08781-3	journal	January 2021
First Monte Carlo Global Analysis of Nucleon Transversity with Lattice QCD Constraints Lin, H. -W.; Melnitchouk, W.; Prokudin, A. Physical Review Letters, Vol. 120, Issue 15 https://doi.org/10.1103/PhysRevLett.120.152502	journal	April 2018
Learning to identify electrons Collado, Julian; Howard, Jessica N.; Faucett, Taylor Physical Review D, Vol. 103, Issue 11 https://doi.org/10.1103/PhysRevD.103.116028	journal	June 2021
Constraints on large- x parton distributions from new weak boson production and deep-inelastic scattering data Accardi, A.; Brady, L. T.; Melnitchouk, W. Physical Review D, Vol. 93, Issue 11 https://doi.org/10.1103/PhysRevD.93.114017	journal	June 2016
Consistent empirical physical formulas for potential energy curves of 38–66Ti isotopes by using neural networks Akkoyun, S.; Bayram, T.; Kara, S. O. Physics of Particles and Nuclei Letters, Vol. 10, Issue 6 https://doi.org/10.1134/S1547477113060022	journal	November 2013
Sample-efficient reinforcement learning for CERN accelerator control Kain, Verena; Hirlander, Simon; Goddard, Brennan Physical Review Accelerators and Beams, Vol. 23, Issue 12 https://doi.org/10.1103/PhysRevAccelBeams.23.124801	journal	December 2020
Trivializing Maps, the Wilson Flow and the HMC Algorithm Lüscher, Martin Communications in Mathematical Physics, Vol. 293, Issue 3 https://doi.org/10.1007/s00220-009-0953-7	journal	November 2009
Online multi-objective particle accelerator optimization of the AWAKE electron beam line for simultaneous emittance and orbit control Scheinker, Alexander; Hirlaender, Simon; Velotti, Francesco Maria AIP Advances, Vol. 10, Issue 5 https://doi.org/10.1063/5.0003423	journal	May 2020
Digital Twin: Enabling Technologies, Challenges and Open Research Fuller, Aidan; Fan, Zhong; Day, Charles IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2998358	journal	January 2020
Toward Machine Learning Optimization of Experimental Design Baydin, Atılım Güneş; Cranmer, Kyle; Manzano, Pablo de Castro Nuclear Physics News, Vol. 31, Issue 1 https://doi.org/10.1080/10619127.2021.1881364	journal	January 2021
Bayesian evaluation of charge yields of fission fragments of U239 Qiao, C. Y.; Pei, J. C.; Wang, Z. A. Physical Review C, Vol. 103, Issue 3 https://doi.org/10.1103/PhysRevC.103.034621	journal	March 2021
Experimental aspects of jet physics at a future EIC Page, B. S.; Chu, Xiaoxuan; Aschenauer, E. C. Physical Review D, Vol. 101, Issue 7 https://doi.org/10.1103/PhysRevD.101.072003	journal	April 2020
Quantified limits of the nuclear landscape Neufcourt, Léo; Cao, Yuchen; Giuliani, Samuel A. Physical Review C, Vol. 101, Issue 4 https://doi.org/10.1103/PhysRevC.101.044307	journal	April 2020
Genetic algorithm enhanced by machine learning in dynamic aperture optimization Li, Yongjun; Cheng, Weixing; Yu, Li Hua Physical Review Accelerators and Beams, Vol. 21, Issue 5 https://doi.org/10.1103/PhysRevAccelBeams.21.054601	journal	May 2018
Constraining transfer cross sections using Bayes' theorem Lovell, A. E.; Nunes, F. M. Physical Review C, Vol. 97, Issue 6 https://doi.org/10.1103/PhysRevC.97.064612	journal	June 2018
Parallel general purpose multiobjective optimization framework with application to electron beam dynamics Neveu, N.; Spentzouris, L.; Adelmann, A. Physical Review Accelerators and Beams, Vol. 22, Issue 5 https://doi.org/10.1103/PhysRevAccelBeams.22.054602	journal	May 2019
New isospin-breaking “USD” Hamiltonians for the sd shell Magilligan, A.; Brown, B. A. Physical Review C, Vol. 101, Issue 6 https://doi.org/10.1103/PhysRevC.101.064312	journal	June 2020
Search for neutrinoless double- β decay in Ge 76 with 26 kg yr of exposure from the Majorana Demonstrator Alvis, S. I.; Arnquist, I. J.; Avignone, F. T. Physical Review C, Vol. 100, Issue 2 https://doi.org/10.1103/PhysRevC.100.025501	journal	August 2019
Approximation by superpositions of a sigmoidal function Cybenko, G. Mathematics of Control, Signals, and Systems, Vol. 2, Issue 4 https://doi.org/10.1007/BF02551274	journal	December 1989
Bayesian inference of the incompressibility, skewness and kurtosis of nuclear matter from empirical pressures in relativistic heavy-ion collisions Xie, Wen-Jie; Li, Bao-An Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 2 https://doi.org/10.1088/1361-6471/abd25a	journal	January 2021
Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe $$\varvec{0\nu \beta \beta }$$ 0 ν β β -decay searches Caldwell, A.; Cossavella, F.; Majorovits, B. The European Physical Journal C, Vol. 75, Issue 7 https://doi.org/10.1140/epjc/s10052-015-3573-8	journal	July 2015
Machine learning n/γ discrimination in CLYC scintillators Doucet, E.; Brown, T.; Chowdhury, P. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 954 https://doi.org/10.1016/j.nima.2018.09.036	journal	February 2020
Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning Boyda, D. L.; Chernodub, M. N.; Gerasimeniuk, N. V. Physical Review D, Vol. 103, Issue 1 https://doi.org/10.1103/PhysRevD.103.014509	journal	January 2021
Theia: an advanced optical neutrino detector Askins, M.; Bagdasarian, Z.; Barros, N. The European Physical Journal C, Vol. 80, Issue 5 https://doi.org/10.1140/epjc/s10052-020-7977-8	journal	May 2020
Lefschetz thimbles decomposition for the Hubbard model on the hexagonal lattice Ulybyshev, Maksim; Winterowd, Christopher; Zafeiropoulos, Savvas Physical Review D, Vol. 101, Issue 1 https://doi.org/10.1103/PhysRevD.101.014508	journal	January 2020
Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks Kranjčević, M.; Riemann, B.; Adelmann, A. Physical Review Accelerators and Beams, Vol. 24, Issue 1 https://doi.org/10.1103/PhysRevAccelBeams.24.014601	journal	January 2021
Deep learning: Extrapolation tool for ab initio nuclear theory Negoita, Gianina Alina; Vary, James P.; Luecke, Glenn R. Physical Review C, Vol. 99, Issue 5 https://doi.org/10.1103/PhysRevC.99.054308	journal	May 2019
DeepRICH: learning deeply Cherenkov detectors Fanelli, Cristiano; Pomponi, Jary Machine Learning: Science and Technology, Vol. 1, Issue 1 https://doi.org/10.1088/2632-2153/ab845a	journal	March 2020
Automated Isotope Identification Algorithm Using Artificial Neural Networks Kamuda, M.; Stinnett, J.; Sullivan, C. J. IEEE Transactions on Nuclear Science, Vol. 64, Issue 7 https://doi.org/10.1109/TNS.2017.2693152	journal	July 2017
Artificial intelligence methods applied for quantitative analysis of natural radioactive sources Medhat, M. E. Annals of Nuclear Energy, Vol. 45 https://doi.org/10.1016/j.anucene.2012.02.013	journal	July 2012
The Search for Double Beta Decay With Germanium Detectors: Past, Present, and Future Avignone III, Frank T.; Elliott, Steven R. Frontiers in Physics, Vol. 7 https://doi.org/10.3389/fphy.2019.00006	journal	February 2019
Model-independent tuning for maximizing free electron laser pulse energy Scheinker, Alexander; Bohler, Dorian; Tomin, Sergey Physical Review Accelerators and Beams, Vol. 22, Issue 8 https://doi.org/10.1103/PhysRevAccelBeams.22.082802	journal	August 2019
Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser Bruchon, Niky; Fenu, Gianfranco; Gaio, Giulio Electronics, Vol. 9, Issue 5 https://doi.org/10.3390/electronics9050781	journal	May 2020
An efficient method to compute the residual phase on a Lefschetz thimble Cristoforetti, M.; Di Renzo, F.; Eruzzi, G. Physical Review D, Vol. 89, Issue 11 https://doi.org/10.1103/PhysRevD.89.114505	journal	June 2014
Neural network perturbation theory and its application to the Born series Kaspschak, Bastian; Meißner, Ulf-G. Physical Review Research, Vol. 3, Issue 2 https://doi.org/10.1103/PhysRevResearch.3.023223	journal	June 2021
Bayesian inference on isospin splitting of nucleon effective mass from giant resonances in ²⁰⁸Pb * Zhang, Zhen; Feng, Xue-Bin; Chen, Lie-Wen Chinese Physics C, Vol. 45, Issue 6 https://doi.org/10.1088/1674-1137/abf428	journal	June 2021
ML-assisted versatile approach to Calorimeter R&D Boldyrev, A.; Derkach, D.; Ratnikov, F. Journal of Instrumentation, Vol. 15, Issue 09 https://doi.org/10.1088/1748-0221/15/09/C09030	journal	September 2020
Neutrinoless double beta decay with ⁸² SeF ₆ and direct ion imaging Nygren, D. R.; Jones, B. J. P.; López-March, N. Journal of Instrumentation, Vol. 13, Issue 03 https://doi.org/10.1088/1748-0221/13/03/P03015	journal	March 2018
Eigenvector Continuation with Subspace Learning Frame, Dillon; He, Rongzheng; Ipsen, Ilse Physical Review Letters, Vol. 121, Issue 3 https://doi.org/10.1103/PhysRevLett.121.032501	journal	July 2018
Neural ordinary differential equation and holographic quantum chromodynamics Hashimoto, Koji; Hu, Hong-Ye; You, Yi-Zhuang Machine Learning: Science and Technology, Vol. 2, Issue 3 https://doi.org/10.1088/2632-2153/abe527	journal	May 2021
Designing Critical Experiments Using Gaussian Process Optimization Grove, T.; Hutchinson, J.; Kleedtke, N. Tranactions - 2019 Winter Meeting, Transactions of the American Nuclear Society - Volume 121 https://doi.org/10.13182/T31031	conference	January 2019
Trees and forests in nuclear physics Carnini, M.; Pastore, A. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 8 https://doi.org/10.1088/1361-6471/ab92e3	journal	July 2020
Maximizing the Information Content of Experiments in Systems Biology Liepe, Juliane; Filippi, Sarah; Komorowski, Michał PLoS Computational Biology, Vol. 9, Issue 1 https://doi.org/10.1371/journal.pcbi.1002888	journal	January 2013
Determination of radioisotopes in gamma-ray spectroscopy using abductive machine learning Abdel-Aal, R. E.; Al-Haddad, M. N. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 391, Issue 2 https://doi.org/10.1016/S0168-9002(97)00391-4	journal	June 1997
Eigenvector continuation as an efficient and accurate emulator for uncertainty quantification König, S.; Ekström, A.; Hebeler, K. Physics Letters B, Vol. 810 https://doi.org/10.1016/j.physletb.2020.135814	journal	November 2020
Deep Learning and Its Application to LHC Physics Guest, Dan; Cranmer, Kyle; Whiteson, Daniel Annual Review of Nuclear and Particle Science, Vol. 68, Issue 1 https://doi.org/10.1146/annurev-nucl-101917-021019	journal	October 2018
How Well Do We Know the Neutron-Matter Equation of State at the Densities Inside Neutron Stars? A Bayesian Approach with Correlated Uncertainties Drischler, C.; Furnstahl, R. J.; Melendez, J. A. Physical Review Letters, Vol. 125, Issue 20 https://doi.org/10.1103/PhysRevLett.125.202702	journal	November 2020
Confronting lattice parton distributions with global QCD analysis Bringewatt, J.; Sato, N.; Melnitchouk, W. Physical Review D, Vol. 103, Issue 1 https://doi.org/10.1103/PhysRevD.103.016003	journal	January 2021
Predicting particle accelerator failures using binary classifiers Rescic, Miha; Seviour, Rebecca; Blokland, Willem Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 955 https://doi.org/10.1016/j.nima.2019.163240	journal	March 2020
Constraining nuclear properties in Mo94 via a Nb93(p,γ)Mo94 total cross section measurement Heim, F.; Scholz, P.; Mayer, J. Physical Review C, Vol. 101, Issue 3 https://doi.org/10.1103/PhysRevC.101.035807	journal	March 2020
Impact of statistical uncertainties on the composition of the outer crust of a neutron star Pastore, A.; Neill, D.; Powell, H. Physical Review C, Vol. 101, Issue 3 https://doi.org/10.1103/PhysRevC.101.035804	journal	March 2020
Precision mass measurement of lightweight self-conjugate nucleus 80Zr Hamaker, A.; Leistenschneider, E.; Jain, R. Nature Physics, Vol. 17, Issue 12 https://doi.org/10.1038/s41567-021-01395-w	journal	November 2021
Calibration of energy density functionals with deformed nuclei Schunck, N.; O’Neal, J.; Grosskopf, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 7 https://doi.org/10.1088/1361-6471/ab8745	journal	June 2020
Improved naive Bayesian probability classifier in predictions of nuclear mass Liu, Yifan; Su, Chen; Liu, Jian Physical Review C, Vol. 104, Issue 1 https://doi.org/10.1103/PhysRevC.104.014315	journal	July 2021
Enhancing nuclear data validation analysis by using machine learning Neudecker, D.; Grosskopf, M.; Herman, M. Nuclear Data Sheets, Vol. 167 https://doi.org/10.1016/j.nds.2020.07.002	journal	July 2020
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei Neufcourt, Léo; Cao, Yuchen; Giuliani, Samuel Physical Review C, Vol. 101, Issue 1 https://doi.org/10.1103/PhysRevC.101.014319	journal	January 2020
Regression Quantiles Koenker, Roger; Bassett, Gilbert Econometrica, Vol. 46, Issue 1 https://doi.org/10.2307/1913643	journal	January 1978
Machine learning action parameters in lattice quantum chromodynamics Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William Physical Review D, Vol. 97, Issue 9 https://doi.org/10.1103/PhysRevD.97.094506	journal	May 2018
Cherenkov detectors fast simulation using neural networks Derkach, Denis; Kazeev, Nikita; Ratnikov, Fedor Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 952 https://doi.org/10.1016/j.nima.2019.01.031	journal	February 2020
Optimization and supervised machine learning methods for fitting numerical physics models without derivatives Bollapragada, Raghu; Menickelly, Matt; Nazarewicz, Witold Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 2 https://doi.org/10.1088/1361-6471/abd009	journal	January 2021
Neutrinoless Double-Beta Decay: Status and Prospects Dolinski, Michelle J.; Poon, Alan W. P.; Rodejohann, Werner Annual Review of Nuclear and Particle Science, Vol. 69, Issue 1 https://doi.org/10.1146/annurev-nucl-101918-023407	journal	October 2019
Reinforcement learning based schemes to manage client activities in large distributed control systems Gao, Y.; Chen, J.; Robertazzi, T. Physical Review Accelerators and Beams, Vol. 22, Issue 1 https://doi.org/10.1103/PhysRevAccelBeams.22.014601	journal	January 2019
Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation Fujimoto, Yuki; Fukushima, Kenji; Murase, Koichi Journal of High Energy Physics, Vol. 2021, Issue 3 https://doi.org/10.1007/JHEP03(2021)273	journal	March 2021
Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics Hanuka, A.; Emma, C.; Maxwell, T. Scientific Reports, Vol. 11, Issue 1 https://doi.org/10.1038/s41598-021-82473-0	journal	February 2021
Exploring experimental conditions to reduce uncertainties in the optical potential Catacora-Rios, M.; King, G. B.; Lovell, A. E. Physical Review C, Vol. 100, Issue 6 https://doi.org/10.1103/PhysRevC.100.064615	journal	December 2019
FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectors Hardin, J.; Williams, M. Journal of Instrumentation, Vol. 11, Issue 10 https://doi.org/10.1088/1748-0221/11/10/P10007	journal	October 2016
Normalizing flows and the real-time sign problem Lawrence, Scott; Yamauchi, Yukari Physical Review D, Vol. 103, Issue 11 https://doi.org/10.1103/PhysRevD.103.114509	journal	June 2021
Recent advances in the quantification of uncertainties in reaction theory Lovell, A. E.; Nunes, F. M.; Catacora-Rios, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 1 https://doi.org/10.1088/1361-6471/abba72	journal	December 2020
Morphology of three-body quantum states from machine learning Huber, David; Marchukov, Oleksandr V.; Hammer, Hans-Werner New Journal of Physics, Vol. 23, Issue 6 https://doi.org/10.1088/1367-2630/ac0576	journal	June 2021
Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks Abdel-Aal, R. E. Neural Computing & Applications, Vol. 11, Issue 1 https://doi.org/10.1007/s005210200012	journal	June 2002
Nonparametric Bayesian approach to extrapolation problems in configuration interaction methods Yoshida, Sota Physical Review C, Vol. 102, Issue 2 https://doi.org/10.1103/PhysRevC.102.024305	journal	August 2020
Determining the jet transport coefficient q ̂ from inclusive hadron suppression measurements using Bayesian parameter estimation Cao, S.; Chen, Y.; Coleman, J. Physical Review C, Vol. 104, Issue 2 https://doi.org/10.1103/PhysRevC.104.024905	journal	August 2021
Erratum to: Monte Carlo study of Lefschetz thimble structure in one-dimensional Thirring model at finite density Fujii, Hirotsugu; Kamata, Syo; Kikukawa, Yoshio Journal of High Energy Physics, Vol. 2016, Issue 9 https://doi.org/10.1007/JHEP09(2016)172	journal	September 2016
Analyzing the Nuclear Interaction: Challenges and New Ideas Ekström, Andreas Frontiers in Physics, Vol. 8 https://doi.org/10.3389/fphy.2020.00029	journal	February 2020
Application of Support Vector Machines to Global Prediction of Nuclear Properties Clark, John W.; Li, Haochen International Journal of Modern Physics B, Vol. 20, Issue 30n31 https://doi.org/10.1142/S0217979206036053	journal	December 2006
Making the Coupled Cluster Correlation Energy Machine-Learnable Margraf, Johannes T.; Reuter, Karsten The Journal of Physical Chemistry A, Vol. 122, Issue 30 https://doi.org/10.1021/acs.jpca.8b04455	journal	July 2018
A machine learning study to identify spinodal clumping in high energy nuclear collisions Steinheimer, Jan; Pang, Long-Gang; Zhou, Kai Journal of High Energy Physics, Vol. 2019, Issue 12 https://doi.org/10.1007/JHEP12(2019)122	journal	December 2019
Bayesian Evaluation of Incomplete Fission Yields Wang, Zi-Ao; Pei, Junchen; Liu, Yue Physical Review Letters, Vol. 123, Issue 12 https://doi.org/10.1103/PhysRevLett.123.122501	journal	September 2019
Constructing approximate shell-model wavefunctions by eigenvector continuation Yoshida, Sota; Shimizu, Noritaka Progress of Theoretical and Experimental Physics https://doi.org/10.1093/ptep/ptac057	journal	April 2022
Bayesian Neural Network improvements to nuclear mass formulae and predictions in the SuperHeavy Elements region Baños Rodríguez, Ubaldo; Zuñiga Vargas, Cristofher; Gonçalves, Marcello EPL (Europhysics Letters), Vol. 127, Issue 4 https://doi.org/10.1209/0295-5075/127/42001	journal	August 2019
hdbscan: Hierarchical density based clustering McInnes, Leland; Healy, John; Astels, Steve The Journal of Open Source Software, Vol. 2, Issue 11 https://doi.org/10.21105/joss.00205	journal	March 2017
Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks Li, A.; Elagin, A.; Fraker, S. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 947 https://doi.org/10.1016/j.nima.2019.162604	journal	December 2019
Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II Scheinker, Alexander; Gessner, Spencer; Emma, Claudio Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 967 https://doi.org/10.1016/j.nima.2020.163902	journal	July 2020
Geant4—a simulation toolkit Agostinelli, S.; Allison, J.; Amako, K. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3 https://doi.org/10.1016/S0168-9002(03)01368-8	journal	July 2003
Machine Learning in Calorimeter optimization Boldyrev, A.; Derkach, D.; Ratnikov, F. Journal of Physics: Conference Series, Vol. 1740 https://doi.org/10.1088/1742-6596/1740/1/012047	journal	January 2021
Bayesian truncation errors in chiral effective field theory: Nucleon-nucleon observables Melendez, J. A.; Wesolowski, S.; Furnstahl, R. J. Physical Review C, Vol. 96, Issue 2 https://doi.org/10.1103/PhysRevC.96.024003	journal	August 2017
AdS/CFT correspondence as a deep Boltzmann machine Hashimoto, Koji Physical Review D, Vol. 99, Issue 10 https://doi.org/10.1103/PhysRevD.99.106017	journal	May 2019
Classification of the impact parameter in nucleus–nucleus collisions by a support vector machine method De Sanctis, J.; Masotti, M.; Bruno, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 36, Issue 1 https://doi.org/10.1088/0954-3899/36/1/015101	journal	October 2008
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems Edelen, Auralee; Neveu, Nicole; Frey, Matthias Physical Review Accelerators and Beams, Vol. 23, Issue 4 https://doi.org/10.1103/PhysRevAccelBeams.23.044601	journal	April 2020
Using machine learning to separate hadronic and electromagnetic interactions in the GlueX forward calorimeter Barsotti, R.; Shepherd, M. R. Journal of Instrumentation, Vol. 15, Issue 05 https://doi.org/10.1088/1748-0221/15/05/P05021	journal	May 2020
Jet flavour classification using DeepJet Bols, E.; Kieseler, J.; Verzetti, M. Journal of Instrumentation, Vol. 15, Issue 12 https://doi.org/10.1088/1748-0221/15/12/P12012	journal	December 2020
Uncertainties of mass extrapolations in Hartree-Fock-Bogoliubov mass models Goriely, S.; Capote, R. Physical Review C, Vol. 89, Issue 5 https://doi.org/10.1103/PhysRevC.89.054318	journal	May 2014
First Simultaneous Extraction of Spin-Dependent Parton Distributions and Fragmentation Functions from a Global QCD Analysis Ethier, J. J.; Sato, N.; Melnitchouk, W. Physical Review Letters, Vol. 119, Issue 13 https://doi.org/10.1103/PhysRevLett.119.132001	journal	September 2017
Neutron Drip Line in the Ca Region from Bayesian Model Averaging Neufcourt, Léo; Cao, Yuchen; Nazarewicz, Witold Physical Review Letters, Vol. 122, Issue 6 https://doi.org/10.1103/PhysRevLett.122.062502	journal	February 2019
Testing trivializing maps in the Hybrid Monte Carlo algorithm Engel, Georg P.; Schaefer, Stefan Computer Physics Communications, Vol. 182, Issue 10 https://doi.org/10.1016/j.cpc.2011.05.004	journal	October 2011
Constraining the symmetry energy with heavy-ion collisions and Bayesian analyses Morfouace, P.; Tsang, C. Y.; Zhang, Y. Physics Letters B, Vol. 799 https://doi.org/10.1016/j.physletb.2019.135045	journal	December 2019
β -delayed one-neutron emission probabilities within a neural network model Wu, Di; Bai, C. L.; Sagawa, H. Physical Review C, Vol. 104, Issue 5 https://doi.org/10.1103/PhysRevC.104.054303	journal	November 2021
Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling Radaideh, Majdi I.; Kozlowski, Tomasz Nuclear Engineering and Technology, Vol. 52, Issue 2 https://doi.org/10.1016/j.net.2019.07.023	journal	February 2020
A neural network approach for identification of gamma-ray spectrum obtained from silicon photomultipliers Jhung, Seungho; Hur, Seop; Cho, Gyuseong Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 954 https://doi.org/10.1016/j.nima.2018.12.019	journal	February 2020
Machine learning the deuteron Keeble, J. W. T.; Rios, A. Physics Letters B, Vol. 809 https://doi.org/10.1016/j.physletb.2020.135743	journal	October 2020
Critical slowing down and error analysis in lattice QCD simulations Schaefer, Stefan; Sommer, Rainer; Virotta, Francesco Nuclear Physics B, Vol. 845, Issue 1 https://doi.org/10.1016/j.nuclphysb.2010.11.020	journal	April 2011
Evaluating 239Pu(n,f) cross sections via machine learning using experimental data, covariances, and measurement features Whewell, B.; Grosskopf, M.; Neudecker, D. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 978 https://doi.org/10.1016/j.nima.2020.164305	journal	October 2020
Halo effective field theory constrains the solar 7Be + p → 8B + γ rate Zhang, Xilin; Nollett, Kenneth M.; Phillips, D. R. Physics Letters B, Vol. 751 https://doi.org/10.1016/j.physletb.2015.11.005	journal	December 2015
Revisiting Bayesian constraints on the transport coefficients of QCD Paquet, Jean-François Nuclear Physics A, Vol. 1005 https://doi.org/10.1016/j.nuclphysa.2020.121749	journal	January 2021
Uncertainty quantification in the nuclear shell model Yoshida, Sota; Shimizu, Noritaka; Togashi, Tomoaki Physical Review C, Vol. 98, Issue 6 https://doi.org/10.1103/PhysRevC.98.061301	journal	December 2018
Deep learning in color: towards automated quark/gluon jet discrimination Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D. Journal of High Energy Physics, Vol. 2017, Issue 1 https://doi.org/10.1007/JHEP01(2017)110	journal	January 2017
Path integral contour deformations for observables in S U ( N ) gauge theory Detmold, William; Kanwar, Gurtej; Lamm, Henry Physical Review D, Vol. 103, Issue 9 https://doi.org/10.1103/PhysRevD.103.094517	journal	May 2021
Statistical error propagation in ab initio no-core full configuration calculations of light nuclei Navarro Pérez, R.; Amaro, J. E.; Ruiz Arriola, E. Physical Review C, Vol. 92, Issue 6 https://doi.org/10.1103/PhysRevC.92.064003	journal	December 2015
Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution Scheinker, Alexander; Edelen, Auralee; Bohler, Dorian Physical Review Letters, Vol. 121, Issue 4 https://doi.org/10.1103/PhysRevLett.121.044801	journal	July 2018
Machine learning for imaging Cherenkov detectors Fanelli, C. Journal of Instrumentation, Vol. 15, Issue 02 https://doi.org/10.1088/1748-0221/15/02/C02012	journal	February 2020
Extrapolating from neural network models: a cautionary tale Pastore, A.; Carnini, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 8 https://doi.org/10.1088/1361-6471/abf08a	journal	June 2021
Quantifying uncertainties and correlations in the nuclear-matter equation of state Drischler, C.; Melendez, J. A.; Furnstahl, R. J. Physical Review C, Vol. 102, Issue 5 https://doi.org/10.1103/PhysRevC.102.054315	journal	November 2020
Present Status and Future Perspectives of the NEXT Experiment Gómez Cadenas, J. J.; Álvarez, V.; Borges, F. I. G. Advances in High Energy Physics, Vol. 2014 https://doi.org/10.1155/2014/907067	journal	January 2014
Online Bayesian Optimization for Beam Alignment in the SECAR Recoil Mass Separator Miskovich, Sara A.; Montes, Fernando; Berg, Georg P. A. arXiv https://doi.org/10.48550/arXiv.2112.01557	preprint	January 2021
Experimental test of an online ion-optics optimizer Amthor, A. M.; Schillaci, Z. M.; Morrissey, D. J. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 895 https://doi.org/10.1016/j.nima.2018.04.001	journal	July 2018
Comparative study of radial basis function and Bayesian neural network approaches in nuclear mass predictions Niu, Z. M.; Fang, J. Y.; Niu, Y. F. Physical Review C, Vol. 100, Issue 5 https://doi.org/10.1103/PhysRevC.100.054311	journal	November 2019
Finite-density Monte Carlo calculations on sign-optimized manifolds Alexandru, Andrei; Bedaque, Paulo F.; Lamm, Henry Physical Review D, Vol. 97, Issue 9 https://doi.org/10.1103/PhysRevD.97.094510	journal	May 2018
Applying Bayesian parameter estimation to relativistic heavy-ion collisions: Simultaneous characterization of the initial state and quark-gluon plasma medium Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A. Physical Review C, Vol. 94, Issue 2 https://doi.org/10.1103/PhysRevC.94.024907	journal	August 2016
Nuclear liquid-gas phase transition with machine learning Wang, Rui; Ma, Yu-Gang; Wada, R. Physical Review Research, Vol. 2, Issue 4 https://doi.org/10.1103/PhysRevResearch.2.043202	journal	November 2020
Monte Carlo study of Lefschetz thimble structure in one-dimensional Thirring model at finite density Fujii, Hirotsugu; Kamata, Syo; Kikukawa, Yoshio Journal of High Energy Physics, Vol. 2015, Issue 12 https://doi.org/10.1007/JHEP12(2015)125	journal	December 2015
Predicting nuclear masses with the kernel ridge regression Wu, X. H.; Zhao, P. W. Physical Review C, Vol. 101, Issue 5 https://doi.org/10.1103/PhysRevC.101.051301	journal	May 2020
Machine-learning-based inversion of nuclear responses Raghavan, Krishnan; Balaprakash, Prasanna; Lovato, Alessandro Physical Review C, Vol. 103, Issue 3 https://doi.org/10.1103/PhysRevC.103.035502	journal	March 2021
Hamilton dynamics for Lefschetz-thimble integration akin to the complex Langevin method Fukushima, K.; Tanizaki, Y. Progress of Theoretical and Experimental Physics, Vol. 2015, Issue 11 https://doi.org/10.1093/ptep/ptv152	journal	September 2015
Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks Karpie, Joseph; Orginos, Kostas; Rothkopf, Alexander Journal of High Energy Physics, Vol. 2019, Issue 4 https://doi.org/10.1007/JHEP04(2019)057	journal	April 2019
Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A. Nature Physics, Vol. 15, Issue 11 https://doi.org/10.1038/s41567-019-0611-8	journal	August 2019
Predictions of nuclear β -decay half-lives with machine learning and their impact on r -process nucleosynthesis Niu, Z. M.; Liang, H. Z.; Sun, B. H. Physical Review C, Vol. 99, Issue 6 https://doi.org/10.1103/PhysRevC.99.064307	journal	June 2019
Towards novel insights in lattice field theory with explainable machine learning Blücher, Stefan; Kades, Lukas; Pawlowski, Jan M. Physical Review D, Vol. 101, Issue 9 https://doi.org/10.1103/PhysRevD.101.094507	journal	May 2020
Parton distributions in the LHC era: MMHT 2014 PDFs Harland-Lang, L. A.; Martin, A. D.; Motylinski, P. The European Physical Journal C, Vol. 75, Issue 5 https://doi.org/10.1140/epjc/s10052-015-3397-6	journal	May 2015
Complex-valued neural networks for machine learning on non-stationary physical data Dramsch, Jesper Sören; Lüthje, Mikael; Christensen, Anders Nymark Computers & Geosciences, Vol. 146 https://doi.org/10.1016/j.cageo.2020.104643	journal	January 2021
Deep Learning the Hohenberg-Kohn Maps of Density Functional Theory Moreno, Javier Robledo; Carleo, Giuseppe; Georges, Antoine Physical Review Letters, Vol. 125, Issue 7 https://doi.org/10.1103/PhysRevLett.125.076402	journal	August 2020
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case Cisbani, E.; Dotto, A. Del; Fanelli, C. Journal of Instrumentation, Vol. 15, Issue 05 https://doi.org/10.1088/1748-0221/15/05/P05009	journal	May 2020
Self-Consistent Equations Including Exchange and Correlation Effects Kohn, W.; Sham, L. J. Physical Review, Vol. 140, Issue 4A, p. A1133-A1138 https://doi.org/10.1103/PhysRev.140.A1133	journal	November 1965
Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects Niu, Z. M.; Liang, H. Z. Physics Letters B, Vol. 778 https://doi.org/10.1016/j.physletb.2018.01.002	journal	March 2018
Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of Pb 208 with Minimal Modeling Assumptions Essick, Reed; Tews, Ingo; Landry, Philippe Physical Review Letters, Vol. 127, Issue 19 https://doi.org/10.1103/PhysRevLett.127.192701	journal	November 2021
Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning Du, Yi-Lun; Zhou, Kai; Steinheimer, Jan The European Physical Journal C, Vol. 80, Issue 6 https://doi.org/10.1140/epjc/s10052-020-8030-7	journal	June 2020
New Generation of Parton Distributions with Uncertainties from Global QCD Analysis Pumplin, Jonathan; Stump, Daniel Robert; Huston, Joey Journal of High Energy Physics, Vol. 2002, Issue 07 https://doi.org/10.1088/1126-6708/2002/07/012	journal	July 2002
Classifying Near-Threshold Enhancement Using Deep Neural Network Sombillo, Denny Lane B.; Ikeda, Yoichi; Sato, Toru Few-Body Systems, Vol. 62, Issue 3 https://doi.org/10.1007/s00601-021-01642-z	journal	July 2021
Method to Solve Quantum Few-Body Problems with Artificial Neural Networks Saito, Hiroki Journal of the Physical Society of Japan, Vol. 87, Issue 7 https://doi.org/10.7566/JPSJ.87.074002	journal	July 2018
Refining mass formulas for astrophysical applications: A Bayesian neural network approach Utama, R.; Piekarewicz, J. Physical Review C, Vol. 96, Issue 4 https://doi.org/10.1103/PhysRevC.96.044308	journal	October 2017
Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests Connell, M. A.; Billig, I.; Phillips, D. R. Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 10 https://doi.org/10.1088/1361-6471/ac215a	journal	September 2021
Extrapolation of nuclear structure observables with artificial neural networks Jiang, W. G.; Hagen, G.; Papenbrock, T. Physical Review C, Vol. 100, Issue 5 https://doi.org/10.1103/PhysRevC.100.054326	journal	November 2019
Relativistic cyclotron radiation detection of tritium decay electrons as a new technique for measuring the neutrino mass Monreal, Benjamin; Formaggio, Joseph A. Physical Review D, Vol. 80, Issue 5 https://doi.org/10.1103/PhysRevD.80.051301	journal	September 2009
Toward emulating nuclear reactions using eigenvector continuation Drischler, C.; Quinonez, M.; Giuliani, P. G. Physics Letters B, Vol. 823 https://doi.org/10.1016/j.physletb.2021.136777	journal	December 2021
Approximation capabilities of multilayer feedforward networks Hornik, Kurt Neural Networks, Vol. 4, Issue 2 https://doi.org/10.1016/0893-6080(91)90009-T	journal	January 1991
Probing heavy ion collisions using quark and gluon jet substructure with machine learning Chien, Yang-Ting Nuclear Physics A, Vol. 982 https://doi.org/10.1016/j.nuclphysa.2018.11.009	journal	February 2019
Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics Shi, Shuzhe; Zhou, Kai; Zhao, Jiaxing Physical Review D, Vol. 105, Issue 1 https://doi.org/10.1103/PhysRevD.105.014017	journal	January 2022
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree Gligorov, V. V.; Williams, M. Journal of Instrumentation, Vol. 8, Issue 02 https://doi.org/10.1088/1748-0221/8/02/P02013	journal	February 2013
Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies Li, Fupeng; Wang, Yongjia; Lü, Hongliang Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 11 https://doi.org/10.1088/1361-6471/abb1f9	journal	October 2020
Alpha half-lives calculation of superheavy nuclei with Q _α -value predictions based on the Bayesian neural network approach Rodríguez, Ubaldo Baños; Vargas, Cristofher Zuñiga; Gonçalves, Marcello Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 11 https://doi.org/10.1088/1361-6471/ab2c86	journal	October 2019
LHCb Topological Trigger Reoptimization Likhomanenko, Tatiana; Ilten, Philip; Khairullin, Egor Journal of Physics: Conference Series, Vol. 664, Issue 8 https://doi.org/10.1088/1742-6596/664/8/082025	journal	December 2015
Self-consistent mean-field models for nuclear structure Bender, Michael; Heenen, Paul-Henri; Reinhard, Paul-Gerhard Reviews of Modern Physics, Vol. 75, Issue 1 https://doi.org/10.1103/RevModPhys.75.121	journal	January 2003
A comparison of Gaussian processes and neural networks for computer model emulation and calibration Myren, Samuel; Lawrence, Earl Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 14, Issue 6 https://doi.org/10.1002/sam.11507	journal	March 2021
Origin of single transverse-spin asymmetries in high-energy collisions Cammarota, Justin; Gamberg, Leonard; Kang, Zhong-Bo Physical Review D, Vol. 102, Issue 5 https://doi.org/10.1103/PhysRevD.102.054002	journal	September 2020
Efficient Global Optimization of Expensive Black-Box Functions Jones, Donald R.; Schonlau, Matthias; Welch, William J. Journal of Global Optimization, Vol. 13, Issue 4, p. 455-492 https://doi.org/10.1023/A:1008306431147	journal	January 1998
Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables Wesolowski, S.; Svensson, I.; Ekström, A. Physical Review C, Vol. 104, Issue 6 https://doi.org/10.1103/PhysRevC.104.064001	journal	December 2021
Model-Free Stabilization by Extremum Seeking Scheinker, Alexander; Krstić, Miroslav SpringerBriefs in Electrical and Computer Engineering https://doi.org/10.1007/978-3-319-50790-3	book	January 2017
First Measurement of Electron Neutrino Appearance in NOvA Adamson, P.; Ader, C.; Andrews, M. Physical Review Letters, Vol. 116, Issue 15 https://doi.org/10.1103/PhysRevLett.116.151806	journal	April 2016
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8 Esfahani, A. Ashtari; Böser, S.; Buzinsky, N. New Journal of Physics, Vol. 22, Issue 3 https://doi.org/10.1088/1367-2630/ab71bd	journal	March 2020
Accurate molecular polarizabilities with coupled cluster theory and machine learning Wilkins, David M.; Grisafi, Andrea; Yang, Yang Proceedings of the National Academy of Sciences, Vol. 116, Issue 9 https://doi.org/10.1073/pnas.1816132116	journal	February 2019
Quantifying modeling uncertainties when combining multiple gravitational-wave detections from binary neutron star sources Kunert, Nina; Pang, Peter T. H.; Tews, Ingo Physical Review D, Vol. 105, Issue 6 https://doi.org/10.1103/PhysRevD.105.L061301	journal	March 2022
Bayesian predictions for A=6 nuclei using eigenvector continuation emulators Djärv, T.; Ekström, A.; Forssén, C. Physical Review C, Vol. 105, Issue 1 https://doi.org/10.1103/PhysRevC.105.014005	journal	January 2022
Bayesian parameter estimation in chiral effective field theory using the Hamiltonian Monte Carlo method Svensson, Isak; Ekström, Andreas; Forssén, Christian Physical Review C, Vol. 105, Issue 1 https://doi.org/10.1103/PhysRevC.105.014004	journal	January 2022
A comparison of machine learning methods for automated gamma-ray spectroscopy Kamuda, Mark; Zhao, Jifu; Huff, Kathryn Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 954 https://doi.org/10.1016/j.nima.2018.10.063	journal	February 2020
Informing nuclear physics via machine learning methods with differential and integral experiments Neudecker, Denise; Cabellos, Oscar; Clark, Alexander R. Physical Review C, Vol. 104, Issue 3 https://doi.org/10.1103/PhysRevC.104.034611	journal	September 2021
S -factor and scattering-parameter extractions from ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}{ \rightarrow }^{7}\mathrm{Be}+\gamma $ Zhang, Xilin; Nollett, Kenneth M.; Phillips, D. R. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 5 https://doi.org/10.1088/1361-6471/ab6a71	journal	March 2020
From noise to information: The transfer function formalism for uncertainty quantification in reconstructing the nuclear density Giuliani, P. G.; Piekarewicz, J. Physical Review C, Vol. 104, Issue 2 https://doi.org/10.1103/PhysRevC.104.024301	journal	August 2021
Bayesian Experimental Design: A Review Chaloner, Kathryn; Verdinelli, Isabella Statistical Science, Vol. 10, Issue 3 https://doi.org/10.1214/ss/1177009939	journal	August 1995
Unsupervised learning for identifying events in active target experiments Solli, R.; Bazin, D.; Hjorth-Jensen, M. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1010 https://doi.org/10.1016/j.nima.2021.165461	journal	September 2021
Machine learning the nuclear mass Gao, Ze-Peng; Wang, Yong-Jia; Lü, Hong-Liang Nuclear Science and Techniques, Vol. 32, Issue 10 https://doi.org/10.1007/s41365-021-00956-1	journal	October 2021
Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements McDonnell, J. D.; Schunck, N.; Higdon, D. Physical Review Letters, Vol. 114, Issue 12 https://doi.org/10.1103/PhysRevLett.114.122501	journal	March 2015
Efficient emulators for scattering using eigenvector continuation Furnstahl, R. J.; Garcia, A. J.; Millican, P. J. Physics Letters B, Vol. 809 https://doi.org/10.1016/j.physletb.2020.135719	journal	October 2020
Equivariant Flow-Based Sampling for Lattice Gauge Theory Kanwar, Gurtej; Albergo, Michael S.; Boyda, Denis Physical Review Letters, Vol. 125, Issue 12 https://doi.org/10.1103/PhysRevLett.125.121601	journal	September 2020
Direct measurements of neutrino mass Formaggio, Joseph A.; de Gouvêa, André Luiz C.; Robertson, R. G. Hamish Physics Reports, Vol. 914 https://doi.org/10.1016/j.physrep.2021.02.002	journal	June 2021
Direct Comparison between Bayesian and Frequentist Uncertainty Quantification for Nuclear Reactions King, G. B.; Lovell, A. E.; Neufcourt, L. Physical Review Letters, Vol. 122, Issue 23 https://doi.org/10.1103/PhysRevLett.122.232502	journal	June 2019
Strange quark suppression from a simultaneous Monte Carlo analysis of parton distributions and fragmentation functions Sato, N.; Andres, C.; Ethier, J. J. Physical Review D, Vol. 101, Issue 7 https://doi.org/10.1103/PhysRevD.101.074020	journal	April 2020
Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J. Physical Review Special Topics - Accelerators and Beams, Vol. 17, Issue 10 https://doi.org/10.1103/PhysRevSTAB.17.101003	journal	October 2014
Efficient machine learning approach for optimizing the timing resolution of a high purity germanium detector Gladen, R. W.; Chirayath, V. A.; Fairchild, A. J. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 981 https://doi.org/10.1016/j.nima.2020.164505	journal	November 2020
Bayesian analysis on interactions of exotic nuclear systems Yang, L.; Lin, C. J.; Zhang, Y. X. Physics Letters B, Vol. 807 https://doi.org/10.1016/j.physletb.2020.135540	journal	August 2020
Deep learning jet modifications in heavy-ion collisions Du, Yi-Lun; Pablos, Daniel; Tywoniuk, Konrad Journal of High Energy Physics, Vol. 2021, Issue 3 https://doi.org/10.1007/JHEP03(2021)206	journal	March 2021
Neural networks for impact parameter determination Bass, S. A.; Bischoff, A.; Maruhn, J. A. Physical Review C, Vol. 53, Issue 5 https://doi.org/10.1103/PhysRevC.53.2358	journal	May 1996
Multimessenger constraints on the neutron-star equation of state and the Hubble constant Dietrich, Tim; Coughlin, Michael W.; Pang, Peter T. H. Science, Vol. 370, Issue 6523 https://doi.org/10.1126/science.abb4317	journal	December 2020
From DFT to machine learning: recent approaches to materials science–a review Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab084b	journal	May 2019
Deep learning based pulse shape discrimination for germanium detectors Holl, P.; Hauertmann, L.; Majorovits, B. The European Physical Journal C, Vol. 79, Issue 6 https://doi.org/10.1140/epjc/s10052-019-6869-2	journal	May 2019
Equation of state for dense nucleonic matter from metamodeling. I. Foundational aspects Margueron, Jérôme; Hoffmann Casali, Rudiney; Gulminelli, Francesca Physical Review C, Vol. 97, Issue 2 https://doi.org/10.1103/PhysRevC.97.025805	journal	February 2018
Bayesian analysis of multimessenger M-R data with interpolated hybrid EoS Ayriyan, A.; Blaschke, D.; Grunfeld, A. G. The European Physical Journal A, Vol. 57, Issue 11 https://doi.org/10.1140/epja/s10050-021-00619-0	journal	November 2021
Bayesian approach for linear optics correction Li, Yongjun; Rainer, Robert; Cheng, Weixing Physical Review Accelerators and Beams, Vol. 22, Issue 1 https://doi.org/10.1103/PhysRevAccelBeams.22.012804	journal	January 2019
Equation of state for dense nucleonic matter from metamodeling. II. Predictions for neutron star properties Margueron, Jérôme; Hoffmann Casali, Rudiney; Gulminelli, Francesca Physical Review C, Vol. 97, Issue 2 https://doi.org/10.1103/PhysRevC.97.025806	journal	February 2018
Hot and dense homogeneous nucleonic matter constrained by observations, experiment, and theory Du, Xingfu; Steiner, Andrew W.; Holt, Jeremy W. Physical Review C, Vol. 99, Issue 2 https://doi.org/10.1103/PhysRevC.99.025803	journal	February 2019
Unsupervised word embeddings capture latent knowledge from materials science literature Tshitoyan, Vahe; Dagdelen, John; Weston, Leigh Nature, Vol. 571, Issue 7763 https://doi.org/10.1038/s41586-019-1335-8	journal	July 2019
Machine learning methods for track classification in the AT-TPC Kuchera, M. P.; Ramanujan, R.; Taylor, J. Z. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 940 https://doi.org/10.1016/j.nima.2019.05.097	journal	October 2019
A.I. for nuclear physics Bedaque, Paulo; Boehnlein, Amber; Cromaz, Mario The European Physical Journal A, Vol. 57, Issue 3 https://doi.org/10.1140/epja/s10050-020-00290-x	journal	March 2021
Thimble regularization at work: From toy models to chiral random matrix theories Di Renzo, F.; Eruzzi, G. Physical Review D, Vol. 92, Issue 8 https://doi.org/10.1103/PhysRevD.92.085030	journal	October 2015
Machine learning and the physical sciences Carleo, Giuseppe; Cirac, Ignacio; Cranmer, Kyle Reviews of Modern Physics, Vol. 91, Issue 4 https://doi.org/10.1103/RevModPhys.91.045002	journal	December 2019
Statistical modelling of neural networks in γ-spectrometry Vigneron, V.; Morel, J.; Lépy, M. C. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 369, Issue 2-3 https://doi.org/10.1016/S0168-9002(96)80068-4	journal	February 1996
Statistical aspects of nuclear mass models Kejzlar, V.; Neufcourt, L.; Nazarewicz, W. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 9 https://doi.org/10.1088/1361-6471/ab907c	journal	July 2020
Microscopically based energy density functionals for nuclei using the density matrix expansion. II. Full optimization and validation Navarro Pérez, R.; Schunck, N.; Dyhdalo, A. Physical Review C, Vol. 97, Issue 5 https://doi.org/10.1103/PhysRevC.97.054304	journal	May 2018
Bayesian Nonparametric Inference of the Neutron Star Equation of State via a Neural Network Han, Ming-Zhe; Jiang, Jin-Liang; Tang, Shao-Peng The Astrophysical Journal, Vol. 919, Issue 1 https://doi.org/10.3847/1538-4357/ac11f8	journal	September 2021
Combining Electromagnetic and Gravitational-Wave Constraints on Neutron-Star Masses and Radii Al-Mamun, Mohammad; Steiner, Andrew W.; Nättilä, Joonas Physical Review Letters, Vol. 126, Issue 6 https://doi.org/10.1103/PhysRevLett.126.061101	journal	February 2021
A New Mass Model for Nuclear Astrophysics: Crossing 200 keV Accuracy Shelley, Matthew; Pastore, Alessandro Universe, Vol. 7, Issue 5 https://doi.org/10.3390/universe7050131	journal	May 2021
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory Tennant, Chris; Carpenter, Adam; Powers, Tom Physical Review Accelerators and Beams, Vol. 23, Issue 11 https://doi.org/10.1103/PhysRevAccelBeams.23.114601	journal	November 2020
Bayesian modeling of the nuclear equation of state for neutron star tidal deformabilities and GW170817 Lim, Y.; Holt, J. W. The European Physical Journal A, Vol. 55, Issue 11 https://doi.org/10.1140/epja/i2019-12917-9	journal	November 2019
Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in Sn 116 , 118 , 120 , 122 , 124 , 130 , 132 , Pb 208 , and Ca 48 Xu, Jun; Xie, Wen-Jie; Li, Bao-An Physical Review C, Vol. 102, Issue 4 https://doi.org/10.1103/PhysRevC.102.044316	journal	October 2020
Flow-based generative models for Markov chain Monte Carlo in lattice field theory Albergo, M. S.; Kanwar, G.; Shanahan, P. E. Physical Review D, Vol. 100, Issue 3 https://doi.org/10.1103/PhysRevD.100.034515	journal	August 2019
Nuclear symmetry energy from neutron skins and pure neutron matter in a Bayesian framework Newton, William G.; Crocombe, Gabriel Physical Review C, Vol. 103, Issue 6 https://doi.org/10.1103/PhysRevC.103.064323	journal	June 2021
Estimation of fusion reaction cross-sections by artificial neural networks Akkoyun, Serkan Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 462 https://doi.org/10.1016/j.nimb.2019.11.014	journal	January 2020
Classifying the pole of an amplitude using a deep neural network Sombillo, Denny Lane B.; Ikeda, Yoichi; Sato, Toru Physical Review D, Vol. 102, Issue 1 https://doi.org/10.1103/PhysRevD.102.016024	journal	July 2020
Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics Phillips, D. R.; Furnstahl, R. J.; Heinz, U. Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 7 https://doi.org/10.1088/1361-6471/abf1df	journal	May 2021
ENDF/B-VIII.0: The 8 th Major Release of the Nuclear Reaction Data Library with CIELO-project Cross Sections, New Standards and Thermal Scattering Data Brown, D. A.; Chadwick, M. B.; Capote, R. Nuclear Data Sheets, Vol. 148 https://doi.org/10.1016/j.nds.2018.02.001	journal	February 2018
Learning representations by back-propagating errors Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Nature, Vol. 323, Issue 6088 https://doi.org/10.1038/323533a0	journal	October 1986
Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory Nandi, Apurba; Qu, Chen; Houston, Paul L. The Journal of Chemical Physics, Vol. 154, Issue 5 https://doi.org/10.1063/5.0038301	journal	February 2021
Bayesian Inference of the Symmetry Energy of Superdense Neutron-rich Matter from Future Radius Measurements of Massive Neutron Stars Xie, Wen-Jie; Li, Bao-An The Astrophysical Journal, Vol. 899, Issue 1 https://doi.org/10.3847/1538-4357/aba271	journal	August 2020
Calculation of nuclear charge radii with a trained feed-forward neural network Wu, Di; Bai, C. L.; Sagawa, H. Physical Review C, Vol. 102, Issue 5 https://doi.org/10.1103/PhysRevC.102.054323	journal	November 2020
Nuclear data evaluation methodology including estimates of covariances Capote, R.; Smith, D. L.; Trkov, A. EPJ Web of Conferences, Vol. 8 https://doi.org/10.1051/epjconf/20100804001	journal	January 2010
Applying Bayesian neural networks to identify pion, kaon and proton in BESII Ye, Xu; Jian, Hou; Kai-En, Zhu Chinese Physics C, Vol. 32, Issue 3 https://doi.org/10.1088/1674-1137/32/3/008	journal	March 2008
Fermions at Finite Density in 2 + 1 Dimensions with Sign-Optimized Manifolds Alexandru, Andrei; Bedaque, Paulo F.; Lamm, Henry Physical Review Letters, Vol. 121, Issue 19 https://doi.org/10.1103/PhysRevLett.121.191602	journal	November 2018
Search for Neutrinoless Double- β Decay with the Complete EXO-200 Dataset Anton, G.; Badhrees, I.; Barbeau, P. S. Physical Review Letters, Vol. 123, Issue 16 https://doi.org/10.1103/PhysRevLett.123.161802	journal	October 2019
Deep learning and the AdS / CFT correspondence Hashimoto, Koji; Sugishita, Sotaro; Tanaka, Akinori Physical Review D, Vol. 98, Issue 4 https://doi.org/10.1103/PhysRevD.98.046019	journal	August 2018
Prediction of neutron-induced fission product yields by a straightforward k -nearest-neighbor algorithm Tong, Liang; He, Ruonan; Yan, Shiwei Physical Review C, Vol. 104, Issue 6 https://doi.org/10.1103/PhysRevC.104.064617	journal	December 2021
Learning and prediction of nuclear stability by neural networks Gazula, S.; Clark, J. W.; Bohr, H. Nuclear Physics A, Vol. 540, Issue 1-2 https://doi.org/10.1016/0375-9474(92)90191-L	journal	April 1992
An automated isotope identification and quantification algorithm for isotope mixtures in low-resolution gamma-ray spectra Kamuda, Mark; Sullivan, Clair J. Radiation Physics and Chemistry, Vol. 155 https://doi.org/10.1016/j.radphyschem.2018.06.017	journal	February 2019
Quantifying uncertainties on fission fragment mass yields with mixture density networks Lovell, A. E.; Mohan, A. T.; Talou, P. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 11 https://doi.org/10.1088/1361-6471/ab9f58	journal	September 2020
NLO PDFs from the ABMP16 fit Alekhin, S.; Blümlein, J.; Moch, S. The European Physical Journal C, Vol. 78, Issue 6 https://doi.org/10.1140/epjc/s10052-018-5947-1	journal	June 2018
New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC Hou, Tie-Jiun; Gao, Jun; Hobbs, T. J. Physical Review D, Vol. 103, Issue 1 https://doi.org/10.1103/PhysRevD.103.014013	journal	January 2021
Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns Shen, Yu-Feng; Pokharel, Reeju; Nizolek, Thomas J. Acta Materialia, Vol. 170 https://doi.org/10.1016/j.actamat.2019.03.026	journal	May 2019
Nuclear charge radii: density functional theory meets Bayesian neural networks Utama, R.; Chen, Wei-Chia; Piekarewicz, J. Journal of Physics G: Nuclear and Particle Physics, Vol. 43, Issue 11 https://doi.org/10.1088/0954-3899/43/11/114002	journal	October 2016
Flow-based sampling for fermionic lattice field theories Albergo, Michael S.; Kanwar, Gurtej; Racanière, Sébastien Physical Review D, Vol. 104, Issue 11 https://doi.org/10.1103/PhysRevD.104.114507	journal	December 2021
Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies Townsend, Jacob; Vogiatzis, Konstantinos D. Journal of Chemical Theory and Computation, Vol. 16, Issue 12 https://doi.org/10.1021/acs.jctc.0c00927	journal	November 2020
Development, characterisation, and deployment of the SNO+ liquid scintillator Anderson, M. R.; Andringa, S.; Anselmo, L. Journal of Instrumentation, Vol. 16, Issue 05 https://doi.org/10.1088/1748-0221/16/05/P05009	journal	May 2021
Stringent constraints on neutron-star radii from multimessenger observations and nuclear theory Capano, Collin D.; Tews, Ingo; Brown, Stephanie M. Nature Astronomy, Vol. 4, Issue 6 https://doi.org/10.1038/s41550-020-1014-6	journal	March 2020
Propagation of statistical uncertainties of Skyrme mass models to simulations of r -process nucleosynthesis Sprouse, T. M.; Navarro Perez, R.; Surman, R. Physical Review C, Vol. 101, Issue 5 https://doi.org/10.1103/PhysRevC.101.055803	journal	May 2020
Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus Ekström, Andreas; Hagen, Gaute Physical Review Letters, Vol. 123, Issue 25 https://doi.org/10.1103/PhysRevLett.123.252501	journal	December 2019
Instantaneous stochastic perturbation theory Lüscher, Martin Journal of High Energy Physics, Vol. 2015, Issue 4 https://doi.org/10.1007/JHEP04(2015)142	journal	April 2015
Background rejection in NEXT using deep neural networks Renner, J.; Farbin, A.; Vidal, J. Muñoz Journal of Instrumentation, Vol. 12, Issue 01 https://doi.org/10.1088/1748-0221/12/01/T01004	journal	January 2017
Deep Learning Based Impact Parameter Determination for the CBM Experiment Kuttan, Manjunath Omana; Steinheimer, Jan; Zhou, Kai Particles, Vol. 4, Issue 1 https://doi.org/10.3390/particles4010006	journal	February 2021
Neural Canonical Transformation with Symplectic Flows Li, Shuo-Hui; Dong, Chen-Xiao; Zhang, Linfeng Physical Review X, Vol. 10, Issue 2 https://doi.org/10.1103/PhysRevX.10.021020	journal	April 2020
Modified empirical formulas and machine learning for α-decay systematics Saxena, G.; Sharma, P. K.; Saxena, Prafulla Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 5 https://doi.org/10.1088/1361-6471/abcd1c	journal	April 2021
Decoding β -decay systematics: A global statistical model for β − half-lives Costiris, N. J.; Mavrommatis, E.; Gernoth, K. A. Physical Review C, Vol. 80, Issue 4 https://doi.org/10.1103/PhysRevC.80.044332	journal	October 2009
The Nuclear Science References (NSR) database and Web Retrieval System Pritychenko, B.; Běták, E.; Kellett, M. A. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 640, Issue 1 https://doi.org/10.1016/j.nima.2011.03.018	journal	June 2011
Search for Majorana neutrinos exploiting millikelvin cryogenics with CUORE Nature, Vol. 604, Issue 7904 https://doi.org/10.1038/s41586-022-04497-4	journal	April 2022
A high-bias, low-variance introduction to Machine Learning for physicists Mehta, Pankaj; Bukov, Marin; Wang, Ching-Hao Physics Reports, Vol. 810 https://doi.org/10.1016/j.physrep.2019.03.001	journal	May 2019
Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks Rrapaj, Ermal; Roggero, Alessandro Physical Review E, Vol. 103, Issue 1 https://doi.org/10.1103/PhysRevE.103.013302	journal	January 2021
New approach to the sign problem in quantum field theories: High density QCD on a Lefschetz thimble Cristoforetti, Marco; Di Renzo, Francesco; Scorzato, Luigi Physical Review D, Vol. 86, Issue 7 https://doi.org/10.1103/PhysRevD.86.074506	journal	October 2012
A fast centrality-meter for heavy-ion collisions at the CBM experiment Omana Kuttan, Manjunath; Steinheimer, Jan; Zhou, Kai Physics Letters B, Vol. 811 https://doi.org/10.1016/j.physletb.2020.135872	journal	December 2020
Machine learning spatial geometry from entanglement features You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang Physical Review B, Vol. 97, Issue 4 https://doi.org/10.1103/PhysRevB.97.045153	journal	January 2018
Efficient modeling of trivializing maps for lattice ϕ 4 theory using normalizing flows: A first look at scalability Del Debbio, Luigi; Marsh Rossney, Joe; Wilson, Michael Physical Review D, Vol. 104, Issue 9 https://doi.org/10.1103/PhysRevD.104.094507	journal	November 2021
Classical and machine learning methods for event reconstruction in NeuLAND Mayer, Jan; Boretzky, Konstanze; Douma, Christiaan Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1013 https://doi.org/10.1016/j.nima.2021.165666	journal	October 2021
Automation and control of laser wakefield accelerators using Bayesian optimization Shalloo, R. J.; Dann, S. J. D.; Gruse, J. -N. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-20245-6	journal	December 2020
A tagger for strange jets based on tracking information using long short-term memory Erdmann, J. Journal of Instrumentation, Vol. 15, Issue 01 https://doi.org/10.1088/1748-0221/15/01/P01021	journal	January 2020
A simple approach towards the sign problem using path optimisation Bursa, Francis; Kroyter, Michael Journal of High Energy Physics, Vol. 2018, Issue 12 https://doi.org/10.1007/JHEP12(2018)054	journal	December 2018
Optimizing multilayer Bayesian neural networks for evaluation of fission yields Wang, Zi-Ao; Pei, Junchen Physical Review C, Vol. 104, Issue 6 https://doi.org/10.1103/PhysRevC.104.064608	journal	December 2021
Bayesian optimization in ab initio nuclear physics Ekström, A.; Forssén, C.; Dimitrakakis, C. Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 9 https://doi.org/10.1088/1361-6471/ab2b14	journal	July 2019
Reducing autocorrelation times in lattice simulations with generative adversarial networks Pawlowski, Jan M.; Urban, Julian M. Machine Learning: Science and Technology, Vol. 1, Issue 4 https://doi.org/10.1088/2632-2153/abae73	journal	October 2020
Reconstruction of D ⁰ meson in d+Au collisions at sNN = 200 GeV by the STAR experiment Kramárik for the STAR collaboration, Lukáš Physica Scripta, Vol. 95, Issue 7 https://doi.org/10.1088/1402-4896/ab9034	journal	May 2020
Data-driven analysis for the temperature and momentum dependence of the heavy-quark diffusion coefficient in relativistic heavy-ion collisions Xu, Yingru; Bernhard, Jonah E.; Bass, Steffen A. Physical Review C, Vol. 97, Issue 1 https://doi.org/10.1103/PhysRevC.97.014907	journal	January 2018

Similar Records

Unsupervised learning for identifying events in active target experiments

Journal Article · 2021 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment · OSTI ID:1786294

Solli, R.; Bazin, D.; Hjorth-Jensen, M.; +2 more

Efficient solutions of fermionic systems using artificial neural networks

Journal Article · 2023 · Frontiers in Physics · OSTI ID:2426646

Nordhagen, Even M.; Kim, Jane M.; Fore, Bryce; +2 more

Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Journal Article · 2023 · Physical Review. C · OSTI ID:2280657

Mroczek, D.; Hjorth-Jensen, M.; Noronha-Hostler, J.; +3 more

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
nuclear binding
machine learning

Title: Colloquium: Machine learning in nuclear physics

Citation Formats

References (325)

Similar Records

Related Subjects