Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
Abstract We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailored for continuous training functions to ex...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of High Energy Physics |
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| Online Access: | https://doi.org/10.1007/JHEP01(2025)025 |
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| author | Byoungjoon Ahn Hyun-Sik Jeong Keun-Young Kim Kwan Yun |
| author_facet | Byoungjoon Ahn Hyun-Sik Jeong Keun-Young Kim Kwan Yun |
| author_sort | Byoungjoon Ahn |
| collection | DOAJ |
| description | Abstract We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailored for continuous training functions to extract the general isotropic bulk metric from entanglement entropy data. To validate our approach, we first apply our machine learning algorithm to holographic entanglement entropy data derived from the Gubser-Rocha and superconductor models, which serve as representative models of strongly coupled matters in holography. Our algorithm successfully extracts the corresponding bulk metrics from these data. Additionally, we extend our methodology to many-body systems by employing entanglement entropy data from a fermionic tight-binding chain at half filling, exemplifying critical one-dimensional systems, and derive the associated bulk metric. We find that the metrics for a tight-binding chain and the Gubser-Rocha model are similar. We speculate this similarity is due to the metallic property of these models. |
| format | Article |
| id | doaj-art-9fd7882f452b4cdbb2ae3481bbd7924c |
| institution | Kabale University |
| issn | 1029-8479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj-art-9fd7882f452b4cdbb2ae3481bbd7924c2025-01-19T12:07:13ZengSpringerOpenJournal of High Energy Physics1029-84792025-01-012025114110.1007/JHEP01(2025)025Holographic reconstruction of black hole spacetime: machine learning and entanglement entropyByoungjoon Ahn0Hyun-Sik Jeong1Keun-Young Kim2Kwan Yun3Department of Physics and Photon Science, Gwangju Institute of Science and TechnologyInstituto de Física Teórica UAM/CSICDepartment of Physics and Photon Science, Gwangju Institute of Science and TechnologyDepartment of Physics and Photon Science, Gwangju Institute of Science and TechnologyAbstract We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailored for continuous training functions to extract the general isotropic bulk metric from entanglement entropy data. To validate our approach, we first apply our machine learning algorithm to holographic entanglement entropy data derived from the Gubser-Rocha and superconductor models, which serve as representative models of strongly coupled matters in holography. Our algorithm successfully extracts the corresponding bulk metrics from these data. Additionally, we extend our methodology to many-body systems by employing entanglement entropy data from a fermionic tight-binding chain at half filling, exemplifying critical one-dimensional systems, and derive the associated bulk metric. We find that the metrics for a tight-binding chain and the Gubser-Rocha model are similar. We speculate this similarity is due to the metallic property of these models.https://doi.org/10.1007/JHEP01(2025)025Gauge-Gravity CorrespondenceHolography and Condensed Matter Physics (AdS/CMT) |
| spellingShingle | Byoungjoon Ahn Hyun-Sik Jeong Keun-Young Kim Kwan Yun Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy Journal of High Energy Physics Gauge-Gravity Correspondence Holography and Condensed Matter Physics (AdS/CMT) |
| title | Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy |
| title_full | Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy |
| title_fullStr | Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy |
| title_full_unstemmed | Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy |
| title_short | Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy |
| title_sort | holographic reconstruction of black hole spacetime machine learning and entanglement entropy |
| topic | Gauge-Gravity Correspondence Holography and Condensed Matter Physics (AdS/CMT) |
| url | https://doi.org/10.1007/JHEP01(2025)025 |
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