Built on

L. Kades, J. M. Pawlowski, A. Rothkopf, M. Scherzer, J. M. Urban, S. J. Wetzel, N. Wink, and F. P. G. Ziegler, Phys. Rev. D 102
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A. Bazavov et al. , Phys. Rev. D 100
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1908
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N. Astrakhantsev, V. V. Braguta, M. D’Elia, A. Y. Kotov, A. A. Nikolaev, and F. Sanfilippo, Phys. Rev. D 102
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A. Rothkopf, Phys. Rept. 858
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1912
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S. Offler, G. Aarts, C. Allton, J. Glesaaen, B. Jäger, S. Kim, M. P. Lombardo, S. M. Ryan, and J.-I. Skullerud, PoS LATTICE2019
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1912
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G. Backus and F. Gilbert, Geophysical Journal International 16
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T. Matsui and H. Satz, Phys. Lett. B178
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M. Jarrell and J. Gubernatis, Physics Reports 269
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R. Rapp and J. Wambach, Adv. Nucl. Phys. 25
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M. Asakawa, Comment on ”heavy quarkonium in extreme conditions” (2020), arXiv:2001.10205 [hep-ph]
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M. Asakawa, T. Hatsuda, and Y. Nakahara, Prog. Part. Nucl. Phys. 46
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H.-T. Ding, Nucl. Phys. A 1005
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F. Karsch, E. Laermann, P. Petreczky, and S. Stickan, Phys. Rev. D 68
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M. Asakawa and T. Hatsuda, Phys. Rev. Lett. 92
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G. Aarts and J. M. Martinez Resco, Nucl. Phys. B 726
2005
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F. Karsch, D. Kharzeev, and H. Satz, Phys. Lett. B637
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C. Bishop, Pattern Recognition and Machine Learning (Springer, 2006)
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K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, and P. Stornati, Phys. Rev. Lett. 126
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J. R. Hershey and P. A. Olsen, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07 4
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D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk, V. A. Goy, S. D. Liubimov, and A. V. Molochkov, Phys. Rev. D 103
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Y. Nagai, A. Tanaka, and A. Tomiya, Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions (2020), arXiv:2010.11900 [hep-lat]
Original
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2021

Machine learning spectral functions in lattice QCD

Chen, S. -Y., Ding, H. -T., Liu, F. -Y. et al.

Understand

We study the inverse problem of reconstructing spectral functions from Euclidean correlation functions via machine learning.

We propose a novel neural network, SVAE, which is based on the variational autoencoder (VAE) and can be naturally applied to the inverse problem.
The prominent feature of the SVAE is that a Shannon-Jaynes entropy term having the ground truth values of spectral functions as prior information is included in the loss function to be minimized.
We train the network with general spectral functions produced from a Gaussian mixture model.

Built on

L. Kades, J. M. Pawlowski, A. Rothkopf, M. Scherzer, J. M. Urban, S. J. Wetzel, N. Wink, and F. P. G. Ziegler, Phys. Rev. D 102
Original
1905
Earlier work this paper cites.
A. Bazavov et al. , Phys. Rev. D 100
Original
1908
Earlier work this paper cites.
N. Astrakhantsev, V. V. Braguta, M. D’Elia, A. Y. Kotov, A. A. Nikolaev, and F. Sanfilippo, Phys. Rev. D 102
Original
1910
Earlier work this paper cites.
A. Rothkopf, Phys. Rept. 858
Original
1912
Earlier work this paper cites.
S. Offler, G. Aarts, C. Allton, J. Glesaaen, B. Jäger, S. Kim, M. P. Lombardo, S. M. Ryan, and J.-I. Skullerud, PoS LATTICE2019
Original
1912
Earlier work this paper cites.
A. N. Tikhonov, Soviet Math. Dokl. 4
1963
Earlier work this paper cites.
G. Backus and F. Gilbert, Geophysical Journal International 16
1968
Earlier work this paper cites.
L. D. McLerran and T. Toimela, Phys. Rev. D 31
1985
Earlier work this paper cites.
T. Matsui and H. Satz, Phys. Lett. B178
1986
Earlier work this paper cites.
M. Jarrell and J. Gubernatis, Physics Reports 269
1996
Earlier work this paper cites.
R. Rapp and J. Wambach, Adv. Nucl. Phys. 25
2000
Earlier work this paper cites.
M. Asakawa, Comment on ”heavy quarkonium in extreme conditions” (2020), arXiv:2001.10205 [hep-ph]
Original
2001
Earlier work this paper cites.
M. Asakawa, T. Hatsuda, and Y. Nakahara, Prog. Part. Nucl. Phys. 46
2001
Earlier work this paper cites.
H.-T. Ding, Nucl. Phys. A 1005
Original
2002
Earlier work this paper cites.
F. Karsch, E. Laermann, P. Petreczky, and S. Stickan, Phys. Rev. D 68
2003
Earlier work this paper cites.
M. Asakawa and T. Hatsuda, Phys. Rev. Lett. 92
2004
Earlier work this paper cites.
G. Aarts and J. M. Martinez Resco, Nucl. Phys. B 726
2005
Earlier work this paper cites.
F. Karsch, D. Kharzeev, and H. Satz, Phys. Lett. B637
2006
Earlier work this paper cites.
C. Bishop, Pattern Recognition and Machine Learning (Springer, 2006)
2006
Earlier work this paper cites.
K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, and P. Stornati, Phys. Rev. Lett. 126
Original
2007
Earlier work this paper cites.
J. R. Hershey and P. A. Olsen, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07 4
2007
Earlier work this paper cites.
D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk, V. A. Goy, S. D. Liubimov, and A. V. Molochkov, Phys. Rev. D 103
Original
2009
Earlier work this paper cites.
Y. Nagai, A. Tanaka, and A. Tomiya, Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions (2020), arXiv:2010.11900 [hep-lat]
Original
2010
Earlier work this paper cites.