A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models

Autor:	Richter-Laskowska, M., Khan, H., Trivedi, N., Maśka, M. M.
Rok vydání:	2018
Předmět:	Condensed Matter - Statistical Mechanics Quantum Physics
Zdroj:	Condens. Matter Phys., 2018, vol. 21, No. 3, 33602
Druh dokumentu:	Working Paper
DOI:	10.5488/CMP.21.33602
Popis:	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 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.09927 Zobrazit plný text záznamu View this record from Arxiv