Machine learning of XY model on a spherical Fibonacci lattice

Abstract

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.

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Cited by in corpus (4)

• Shell-shaped atomic gases
• Vortex structure and spectrum of atomic Fermi superfluid in a spherical bubble trap
• Localization of quantum walk with classical randomness: Comparison between manual methods and supervised machine learning
• Machine learning of the Ising model on a spherical Fibonacci lattice