Machine learning action parameters in lattice quantum chromodynamics

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

doi:10.1103/PhysRevD.97.094506

Title: Machine learning action parameters in lattice quantum chromodynamics

Journal Article · 16 May 2018 · Physical Review. D.

DOI: https://doi.org/10.1103/PhysRevD.97.094506 · OSTI ID:1437340

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

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Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: SC0010495; SC0011090; SC0018121; AC05-06OR23177

OSTI ID:: 1437340

Journal Information:: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 9 Vol. 97; ISSN PRVDAQ; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

References (61)

Quantum Chromodynamics on the Lattice Gattringer, Christof; Lang, Christian B. Lecture Notes in Physics https://doi.org/10.1007/978-3-642-01850-3	book	January 2010
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position Fukushima, Kunihiko Biological Cybernetics, Vol. 36, Issue 4 https://doi.org/10.1007/BF00344251	journal	April 1980
The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk Madras, Neal; Sokal, Alan D. Journal of Statistical Physics, Vol. 50, Issue 1-2 https://doi.org/10.1007/BF01022990	journal	January 1988
Block renormalization group for Euclidean Fermions Balaban, Tadeusz; O'Carroll, Michael; Schor, Ricardo Communications in Mathematical Physics, Vol. 122, Issue 2 https://doi.org/10.1007/BF01257414	journal	June 1989
A new class of metric divergences on probability spaces and its applicability in statistics Österreicher, Ferdinand; Vajda, Igor Annals of the Institute of Statistical Mathematics, Vol. 55, Issue 3 https://doi.org/10.1007/BF02517812	journal	September 2003
Erratum: Properties and uses of the Wilson flow in lattice QCD Lüscher, Martin Journal of High Energy Physics, Vol. 2014, Issue 3 https://doi.org/10.1007/JHEP03(2014)092	journal	March 2014
Properties and uses of the Wilson flow in lattice QCD Lüscher, Martin Journal of High Energy Physics, Vol. 2010, Issue 8 https://doi.org/10.1007/JHEP08(2010)071	journal	August 2010
The renormalization group and the ε expansion Wilson, K. Physics Reports, Vol. 12, Issue 2 https://doi.org/10.1016/0370-1573(74)90023-4	journal	August 1974
Hybrid Monte Carlo Duane, Simon; Kennedy, A. D.; Pendleton, Brian J. Physics Letters B, Vol. 195, Issue 2 https://doi.org/10.1016/0370-2693(87)91197-X	journal	September 1987
Fixed point actions for Wilson fermions Wiese, U. -J. Physics Letters B, Vol. 315, Issue 3-4 https://doi.org/10.1016/0370-2693(93)91634-Y	journal	October 1993
Representations of the rotation reflection symmetry group of the four-dimensional cubic lattice Mandula, Jeffrey E.; Zweig, George; Govaerts, Jan Nuclear Physics B, Vol. 228, Issue 1 https://doi.org/10.1016/0550-3213(83)90399-1	journal	November 1983
Multi-grid Monte Carlo (II). Two-dimensional XY model Edwards, Robert G.; Goodman, Jonathan; Sokal, Alan D. Nuclear Physics B, Vol. 354, Issue 2-3 https://doi.org/10.1016/0550-3213(91)90357-4	journal	May 1991
Multi-grid Monte Carlo (III). Two-dimensional O(4)-symmetric non-linear σ-model Edwards, Robert G.; JoséFerreira, Sabino; Goodman, Jonathan Nuclear Physics B, Vol. 380, Issue 3 https://doi.org/10.1016/0550-3213(92)90262-A	journal	August 1992
Perfect lattice action for asymptotically free theories Hasenfratz, P.; Niedermayer, F. Nuclear Physics B, Vol. 414, Issue 3 https://doi.org/10.1016/0550-3213(94)90261-5	journal	February 1994
Moments of nucleon spin-dependent generalized parton distributions Schroers, W.; Brower, R. C.; Dreher, P. Nuclear Physics B - Proceedings Supplements, Vol. 129-130 https://doi.org/10.1016/S0920-5632(03)02750-6	journal	March 2004
Applications of neural networks to the studies of phase transitions of two-dimensional Potts models Li, C. -D.; Tan, D. -R.; Jiang, F. -J. Annals of Physics, Vol. 391 https://doi.org/10.1016/j.aop.2018.02.018	journal	April 2018
The dropout learning algorithm Baldi, Pierre; Sadowski, Peter Artificial Intelligence, Vol. 210 https://doi.org/10.1016/j.artint.2014.02.004	journal	May 2014
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
The Chroma Software System for Lattice QCD Edwards, Robert G.; Joó, Bálint Nuclear Physics B - Proceedings Supplements, Vol. 140 https://doi.org/10.1016/j.nuclphysbps.2004.11.254	journal	March 2005
Nuclear physics from lattice QCD Beane, S. R.; Detmold, W.; Orginos, K. Progress in Particle and Nuclear Physics, Vol. 66, Issue 1 https://doi.org/10.1016/j.ppnp.2010.08.002	journal	January 2011
Analysis of a complex of statistical variables into principal components. Hotelling, H. Journal of Educational Psychology, Vol. 24, Issue 6 https://doi.org/10.1037/h0071325	journal	January 1933
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit Hahnloser, Richard H. R.; Sarpeshkar, Rahul; Mahowald, Misha A. Nature, Vol. 405, Issue 6789 https://doi.org/10.1038/35016072	journal	June 2000
Machine learning phases of matter Carrasquilla, Juan; Melko, Roger G. Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4035	journal	February 2017
Lattice QCD input for nuclear structure and reactions Davoudi, Zohreh EPJ Web of Conferences, Vol. 175 https://doi.org/10.1051/epjconf/201817501022	journal	January 2018
A new Directed Divergence Measure and its Characterization∗ Lin, J.; Wong, S. K. M. International Journal of General Systems, Vol. 17, Issue 1 https://doi.org/10.1080/03081079008935097	journal	May 1990
LIII. On lines and planes of closest fit to systems of points in space Pearson, Karl The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 2, Issue 11 https://doi.org/10.1080/14786440109462720	journal	November 1901
Lattice QCD with mixed actions collaboration, Ukqcd; Bowler, Ken C.; Joó, Balint Journal of High Energy Physics, Vol. 2005, Issue 08 https://doi.org/10.1088/1126-6708/2005/08/003	journal	August 2005
Relations Between two sets of Variates Hotelling, H. Biometrika, Vol. 28, Issue 3-4 https://doi.org/10.1093/biomet/28.3-4.321	journal	December 1936
Notes on Continuous Stochastic Phenomena Moran, P. A. P. Biometrika, Vol. 37, Issue 1-2 https://doi.org/10.1093/biomet/37.1-2.17	journal	January 1950
Finite-lattice approximations to renormalization groups Bell, Thomas L.; Wilson, Kenneth G. Physical Review B, Vol. 11, Issue 9 https://doi.org/10.1103/PhysRevB.11.3431	journal	May 1975
Learning thermodynamics with Boltzmann machines Torlai, Giacomo; Melko, Roger G. Physical Review B, Vol. 94, Issue 16 https://doi.org/10.1103/PhysRevB.94.165134	journal	October 2016
Discovering phase transitions with unsupervised learning Wang, Lei Physical Review B, Vol. 94, Issue 19 https://doi.org/10.1103/PhysRevB.94.195105	journal	November 2016
Accelerated Monte Carlo simulations with restricted Boltzmann machines Huang, Li; Wang, Lei Physical Review B, Vol. 95, Issue 3 https://doi.org/10.1103/PhysRevB.95.035105	journal	January 2017
Self-learning Monte Carlo method Liu, Junwei; Qi, Yang; Meng, Zi Yang Physical Review B, Vol. 95, Issue 4 https://doi.org/10.1103/PhysRevB.95.041101	journal	January 2017
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
Confinement of quarks Wilson, Kenneth G. Physical Review D, Vol. 10, Issue 8 https://doi.org/10.1103/PhysRevD.10.2445	journal	October 1974
Measurement of the π − p elastic scattering cross section near 180° between 600 and 1280 MeV/ c Richards, T. J.; Crabb, D. G.; Keller, R. Physical Review D, Vol. 10, Issue 1 https://doi.org/10.1103/PhysRevD.10.45	journal	July 1974
Multigrid Monte Carlo algorithms for SU(2) lattice gauge theory: Two versus four dimensions Grabenstein, Martin; Pinn, Klaus Physical Review D, Vol. 50, Issue 11 https://doi.org/10.1103/PhysRevD.50.6998	journal	December 1994
I = 2 π π scattering from fully-dynamical mixed-action lattice QCD Beane, Silas R.; Bedaque, Paulo F.; Orginos, Kostas Physical Review D, Vol. 73, Issue 5 https://doi.org/10.1103/PhysRevD.73.054503	journal	March 2006
Light asymmetric dark matter on the lattice: SU(2) technicolor with two fundamental flavors Lewis, Randy; Pica, Claudio; Sannino, Francesco Physical Review D, Vol. 85, Issue 1 https://doi.org/10.1103/PhysRevD.85.014504	journal	January 2012
Dark nuclei. II. Nuclear spectroscopy in two-color QCD Detmold, William; McCullough, Matthew; Pochinsky, Andrew Physical Review D, Vol. 90, Issue 11 https://doi.org/10.1103/PhysRevD.90.114506	journal	December 2014
Multiscale Monte Carlo equilibration: Pure Yang-Mills theory Endres, Michael G.; Brower, Richard C.; Detmold, William Physical Review D, Vol. 92, Issue 11 https://doi.org/10.1103/PhysRevD.92.114516	journal	December 2015
Multiscale Monte Carlo equilibration: Two-color QCD with two fermion flavors Detmold, William; Endres, Michael G. Physical Review D, Vol. 94, Issue 11 https://doi.org/10.1103/PhysRevD.94.114502	journal	December 2016
Exploring cluster Monte Carlo updates with Boltzmann machines Wang, Lei Physical Review E, Vol. 96, Issue 5 https://doi.org/10.1103/PhysRevE.96.051301	journal	November 2017
Machine Learning Topological Invariants with Neural Networks Zhang, Pengfei; Shen, Huitao; Zhai, Hui Physical Review Letters, Vol. 120, Issue 6 https://doi.org/10.1103/PhysRevLett.120.066401	journal	February 2018
Multigrid Monte Carlo Method for Lattice Field Theories Goodman, Jonathan; Sokal, Alan D. Physical Review Letters, Vol. 56, Issue 10 https://doi.org/10.1103/PhysRevLett.56.1015	journal	March 1986
Machine Learning Phases of Strongly Correlated Fermions Ch’ng, Kelvin; Carrasquilla, Juan; Melko, Roger G. Physical Review X, Vol. 7, Issue 3 https://doi.org/10.1103/PhysRevX.7.031038	journal	August 2017
Divergence measures based on the Shannon entropy Lin, J. IEEE Transactions on Information Theory, Vol. 37, Issue 1 https://doi.org/10.1109/18.61115	journal	January 1991
Self-organization in a perceptual network Linsker, R. Computer, Vol. 21, Issue 3 https://doi.org/10.1109/2.36	journal	March 1988
A new metric for probability distributions Endres, D. M.; Schindelin, J. E. IEEE Transactions on Information Theory, Vol. 49, Issue 7 https://doi.org/10.1109/TIT.2003.813506	journal	July 2003
Cross-Validatory Choice and Assessment of Statistical Predictions Stone, M. Journal of the Royal Statistical Society: Series B (Methodological), Vol. 36, Issue 2 https://doi.org/10.1111/j.2517-6161.1974.tb00994.x	journal	January 1974
Solving the quantum many-body problem with artificial neural networks Carleo, Giuseppe; Troyer, Matthias Science, Vol. 355, Issue 6325 https://doi.org/10.1126/science.aag2302	journal	February 2017
Review of lattice results concerning low-energy particle physics: Flavour Lattice Averaging Group (FLAG) Aoki, S.; Aoki, Y.; Bečirević, D. The European Physical Journal C, Vol. 77, Issue 2 https://doi.org/10.1140/epjc/s10052-016-4509-7	journal	February 2017
Introduction Rothe, Heinz J. Lattice Gauge Theories https://doi.org/10.1142/9789814365871_0001	book	March 2012
Thermodynamics of strong-interaction matter from lattice QCD Ding, Heng-Tong; Karsch, Frithjof; Mukherjee, Swagato International Journal of Modern Physics E, Vol. 24, Issue 10 https://doi.org/10.1142/S0218301315300076	journal	October 2015
Perfect Actions: From the Theoretical Background to Recent Developments Hasenfratz, Peter Progress of Theoretical Physics Supplement, Vol. 131 https://doi.org/10.1143/PTPS.131.189	journal	January 1998
Finding Minimum Entropy Codes Barlow, H. B.; Kaushal, T. P.; Mitchison, G. J. Neural Computation, Vol. 1, Issue 3 https://doi.org/10.1162/neco.1989.1.3.412	journal	September 1989
On Information and Sufficiency Kullback, S.; Leibler, R. A. The Annals of Mathematical Statistics, Vol. 22, Issue 1 https://doi.org/10.1214/aoms/1177729694	journal	March 1951
Principles of neurodynamics. Perceptrons and the theory of brain mechanisms Rosenblatt, Frank https://doi.org/10.21236/AD0256582	report	March 1961
The Contiguity Ratio and Statistical Mapping Geary, R. C. The Incorporated Statistician, Vol. 5, Issue 3 https://doi.org/10.2307/2986645	journal	November 1954
Parallel Distributed Processing Rumelhart, David E.; McClelland, James L. https://doi.org/10.7551/mitpress/5236.001.0001	book	January 1986

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