Building imaginary-time thermal field theory with artificial neural networks
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913536734658560 |
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| author | Xu, Tian Wang, Lingxiao He, Lianyi Zhou, Kai Jiang, Yin |
| author_facet | Xu, Tian Wang, Lingxiao He, Lianyi Zhou, Kai Jiang, Yin |
| contents | In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on system configurations governed by the Boltzmann factor, $e^{-S}$ at different temperatures within the imaginary time formalism of thermal field theory. We focus on 0+1 dimensional quantum field with kink/anti-kink configurations to demonstrate the feasibility of the method. The 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 the detailed exploration of phase diagrams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building imaginary-time thermal field theory with artificial neural networks Xu, Tian Wang, Lingxiao He, Lianyi Zhou, Kai Jiang, Yin High Energy Physics - Lattice Computational Physics In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on system configurations governed by the Boltzmann factor, $e^{-S}$ at different temperatures within the imaginary time formalism of thermal field theory. We focus on 0+1 dimensional quantum field with kink/anti-kink configurations to demonstrate the feasibility of the method. The 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 the detailed exploration of phase diagrams. |
| title | Building imaginary-time thermal field theory with artificial neural networks |
| topic | High Energy Physics - Lattice Computational Physics |
| url | https://arxiv.org/abs/2405.10493 |