5 research outputs found
A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models
The Berezinskii-Kosterlitz-Thouless transition is a very specific phase
transition where all thermodynamic quantities are smooth. Therefore, it is
difficult to determine the critical temperature in a precise way. In this paper
we demonstrate how neural networks can be used to perform this task. In
particular, we study how the accuracy of the transition identification depends
on the way the neural networks are trained. We apply our approach to three
different systems: (i) the classical XY model, (ii) the phase-fermion model,
where classical and quantum degrees of freedom are coupled and (iii) the
quantum XY model.Comment: 11 pages, 7 figure