Պահպանված է:
| Հիմնական հեղինակներ: | , , , , , , , |
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| Ձևաչափ: | Preprint |
| Հրապարակվել է: |
2021
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| Խորագրեր: | |
| Առցանց հասանելիություն: | https://arxiv.org/abs/2109.00254 |
| Ցուցիչներ: |
Ավելացրեք ցուցիչ
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1866916429744308224 |
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| author | Song, Chen-Hui Gao, Qu-Cheng Hou, Xu-Yang Wang, Xin Zhou, Zheng He, Yan Guo, Hao Chien, Chih-Chun |
| author_facet | Song, Chen-Hui Gao, Qu-Cheng Hou, Xu-Yang Wang, Xin Zhou, Zheng He, Yan Guo, Hao Chien, Chih-Chun |
| contents | We study the XY model on a spherical surface inspired by recently realized spherically confined atomic gases. Instead of a traditional latitude-longitude lattice, we introduce a much more homogeneous spherical lattice, the Fibonacci lattice, and use classical Monte Carlo simulations to determine spin configurations. The results clearly show that topological defects, in the form of vortices, must exist in the stable configuration on a sphere but vanish in a plane due to a mathematical theorem. Using these spin configurations as training samples, we propose a graph-convolutional-network based method to recognize different phases, and successfully predict the phase transition temperature. We also apply the density-based spatial clustering of applications with noise, a powerful machine learning algorithm, to monitor the merging path of two vortices with different topological charges on the sphere during Monte Carlo simulations. Our results provide reliable predictions for future space-based experiments on ultracold atomic gases confined on spherical lattice in the microgravity environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_00254 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Machine learning of XY model on a spherical Fibonacci lattice Song, Chen-Hui Gao, Qu-Cheng Hou, Xu-Yang Wang, Xin Zhou, Zheng He, Yan Guo, Hao Chien, Chih-Chun Quantum Gases We study the XY model on a spherical surface inspired by recently realized spherically confined atomic gases. Instead of a traditional latitude-longitude lattice, we introduce a much more homogeneous spherical lattice, the Fibonacci lattice, and use classical Monte Carlo simulations to determine spin configurations. The results clearly show that topological defects, in the form of vortices, must exist in the stable configuration on a sphere but vanish in a plane due to a mathematical theorem. Using these spin configurations as training samples, we propose a graph-convolutional-network based method to recognize different phases, and successfully predict the phase transition temperature. We also apply the density-based spatial clustering of applications with noise, a powerful machine learning algorithm, to monitor the merging path of two vortices with different topological charges on the sphere during Monte Carlo simulations. Our results provide reliable predictions for future space-based experiments on ultracold atomic gases confined on spherical lattice in the microgravity environment. |
| title | Machine learning of XY model on a spherical Fibonacci lattice |
| topic | Quantum Gases |
| url | https://arxiv.org/abs/2109.00254 |