\begin{document}$ e^{-S} $\end{document}, at different temperatures within the imaginary time formalism of thermal field theory. Specifically, we focus on the 0+1 dimensional quantum field with kink/anti-kink configurations to demonstrate the feasibility of the method. Continuous-mixture autoregressive networks (CANs) enable the construction of accurate effective actions with tractable probability density estimation. Our numerical results demonstrate that this methodology not only facilitates the construction of effective actions at specified temperatures but also adeptly estimates the action at intermediate temperatures using data from both lower and higher temperature ensembles. This capability is especially valuable for detailed exploration of phase diagrams."> Building imaginary-time thermal field theory with artificial neural networks -
  • [1]

    S. Muroya, A. Nakamura, C. Nonakaet al., Prog. Theor. Phys.110, 615 (2003), arXiv: hep-lat/0306031

  • [2]

    C. Ratti, Rept. Prog. Phys.81(8), 084301 (2018), arXiv: 1804.07810[hep-lat]

  • [3]

    S. Durret al. (BMW), Science322, 1224 (2008), arXiv: 0906.3599[hep-lat]

  • [4]

    E. Ballini, G. Clemente, M. D'Eliaet al., PoSLATTICE2023, 224 (2024)

  • [5]

    J. Greensite, Lect. Notes Phys.821, 1 (2011)

  • [6]

    J. Bardeen, L. N. Cooper, and J. R. Schrieffer, Phys. Rev.108, 1175 (1957)

  • [7]

    J. M. Kosterlitz and D. J. Thouless, J. Phys. C6, 1181 (1973)

  • [8]

    Y. Nambu, Phys. Rev. D10, 4262 (1974)

  • [9]

    B. J. Harrington and H. K. Shepard, Phys. Rev. D17, 2122 (1978)

  • [10]

    H. Fritzsch, Phys. Lett. B256, 75 (1991)

  • [11]

    T. Schäfer and E. V. Shuryak, Rev. Mod. Phys.70, 323 (1998), arXiv: hep-ph/9610451[hep-ph]

  • [12]

    A. Altland and B. D. Simons,Condensed matter field theory(2010)

  • [13]

    H. Sonoda and H. Suzuki, PTEP2021(2), 023B05 (2021), arXiv: 2012.03568[hep-th]

  • [14]

    S. Schaeferet al. (ALPHA), Nucl. Phys. B845, 93 (2011), arXiv: 1009.5228[hep-lat]

  • [15]

    G. Pan and Z. Y. Meng, doi: 10.1016/B978-0-323-90800-9.00095-0, arXiv: 2204.08777[cond-mat.str-el]

  • [16]

    J. Carrasquilla and R. G. Melko, Nature Phys.13, 431 (2017), arXiv: 1605.01735[cond-mat.str-el]

  • [17]

    G. Carleo and M. Troyer, Science355(6325), 602 (2017)

  • [18]

    G. Carleo, I. Cirac, K. Cranmeret al., Rev. Mod. Phys.91(4), 045002 (2019), arXiv: 1903.10563[physics.comp-ph]

  • [19]

    K. Zhou, L. Wang, L. G. Panget al., Prog. Part. Nucl. Phys.135, 104084 (2024), arXiv: 2303.15136[hep-ph]

  • [20]

    D. Wu, L. Wang, and P. Zhang, Phys. Rev. Lett.122(8), 080602 (2019)

  • [21]

    O. Sharir, Y. Levine, N. Wieset al., Phys. Rev. Lett.124(2), 020503 (2020)

  • [22]

    D. Luo, Z. Chen, K. Huet al., Phys. Rev. Res.5(1), 013216 (2023)

  • [23]

    A. Fujita, J. R. Sato, H. M. Garay-Malpartidaet al.,Modeling gene expression regulatory networks with the sparse vector autoregressive model, BMCsystemsbiology1, 1 (2007)

  • [24]

    S. Goldman, J. Li, and C. W. Coley,Generating molecular fragmentation graphs with autoregressive neural networks, AnalyticalChemistry, (2024)

  • [25]

    L. Wang, Y. Jiang, L. Heet al., Chin. Phys. Lett.39(12), 120502 (2022), arXiv: 2005.04857[cond-mat.dis-nn]

  • [26]

    L. Wang, Y. Jiang, and K. Zhou, arXiv: physics.comp-ph/2007.01037

  • [27]

    P. E. Shanahan, A. Trewartha, and W. Detmold, Phys. Rev. D97(9), 094506 (2018), arXiv: 1801.05784[hep-lat]

  • [28]

    Y. T. Song, In Journal of Physics: Conference Series2649, 012055 (2023)

  • [29]

    S. Blücher, L. Kades, J. M. Pawlowskiet al., Phys. Rev. D101(9), 094507 (2020), arXiv: 2003.01504[hep-lat]

  • [30]

    M. Favoni, A. Ipp, D. I. Mülleret al., Phys. Rev. Lett.128(3), 3 (2022), arXiv: 2012.12901[hep-lat]

  • [31]

    B. Allen, Phys. Rev. D33, 3640 (1986)

  • [32]

    F. Bruckmann, Eur. Phys. J. ST152, 61 (2007), arXiv: 0706.2269[hep-th]

  • [33]

    M. A. Lopez-Ruiz, T. Yepez-Martinez, A. Szczepaniaket al., Nucl. Phys. A966, 324 (2017), arXiv: 1605.08017[nucl-th]

  • [34]

    G. 't Hooft, arXiv: hep-th/0010225

  • [35]

    S. Chen, O. Savchuk, S. Zhenget al., Phys. Rev. D107(5), 056001 (2023), arXiv: 2211.03470[hep-lat]

  • [36]

    T. Schäfer, arXiv: hep-lat/0411010

  • [37]

    J. X. Pan and K. T. Fang,Maximum Likelihood Estimation, Growth Eurve Models and Statistical Diagnostics, pages 77-158, 2002

  • [38]

    M. Germain, K. Gregor, I. Murrayet al., arXiv: 1502.03509[cs.LG]

  • [39]

    G. Aarts, B. Lucini, and C. Park, Phys. Rev. D109(3), 034521 (2024), arXiv: 2309.15002[hep-lat]

  • [40]

    D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuket al., Phys. Rev. D103(1), 014509 (2021), arXiv: 2009.10971[hep-lat]

  • [41]

    A. Palermo, L. Anderlini, M. P. Lombardoet al., PoSLATTICE2021, 030 (2022), arXiv: 2111.05216[hep-lat]

  • [42]

    N. Sale, B. Lucini, and J. Giansiracusa, Phys. Rev. D107(3), 034501 (2023), arXiv: 2207.13392[hep-lat]

  • [43]

    D. Spitz, J. M. Urban, and J. M. Pawlowski, Phys. Rev. D107(3), 034506 (2023), arXiv: 2208.03955[hep-lat]

  • [44]

    D. Diakonov, N. Gromov, V. Petrovet al., Phys. Rev. D70, 036003 (2004), arXiv: hep-th/0404042[hep-th]

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