Machine learning in phase transition analysis of lattice quantum gravity
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866911711964954624 |
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| author | Ambjorn, Jan Drogosz, Zbigniew Gizbert-Studnicki, Jakub Görlich, Andrzej Németh, Dániel Reitz, Marcus |
| author_facet | Ambjorn, Jan Drogosz, Zbigniew Gizbert-Studnicki, Jakub Görlich, Andrzej Németh, Dániel Reitz, Marcus |
| contents | Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning in phase transition analysis of lattice quantum gravity Ambjorn, Jan Drogosz, Zbigniew Gizbert-Studnicki, Jakub Görlich, Andrzej Németh, Dániel Reitz, Marcus High Energy Physics - Lattice High Energy Physics - Theory Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters. |
| title | Machine learning in phase transition analysis of lattice quantum gravity |
| topic | High Energy Physics - Lattice High Energy Physics - Theory |
| url | https://arxiv.org/abs/2510.02159 |