Please use this identifier to cite or link to this item: https://doi.org/10.1088/1367-2630/16/1/013004
Title: Pairing correlations in the two-layer attractive Hubbard model
Authors: Zujev, A
Scalettar, R.T
Batrouni, G.G 
Sengupta, P
Keywords: Attractive interactions
Attractive systems
Ground state properties
Low temperatures
Pairing correlations
Periodic Anderson model
Quantum monte carlo
Repulsive interactions
Hamiltonians
Wave functions
Fermi surface
Issue Date: 2014
Citation: Zujev, A, Scalettar, R.T, Batrouni, G.G, Sengupta, P (2014). Pairing correlations in the two-layer attractive Hubbard model. New Journal of Physics 16 : 13004. ScholarBank@NUS Repository. https://doi.org/10.1088/1367-2630/16/1/013004
Rights: Attribution 4.0 International
Abstract: Studies of systems with two fermionic bands (or equivalently, layers) with repulsive interaction strength U have a long history, with the periodic Anderson model (PAM) being one of the most frequently considered Hamiltonians. In this paper, we use quantum Monte Carlo to study analogous issues for attractive interactions. As in the PAM, we focus on a case where one band (layer) is uncorrelated (U = 0), and the effect of hybridization V between the bands (layers) on the pairing correlations. A key difference with the PAM is that there is no sign problem, so that we are better able to explore the physics of doped bilayer attractive systems at low temperatures (except in the case of exponentially small transition temperatures) whereas ground state properties of repulsive models can be determined only at half-filling. For small V , pairing in the U < 0 layer induces pairing in the U = 0 layer. At larger V superfluidity is suppressed at the low but finite T at which the quantum Monte Carlo was performed. The quantum Monte Carlo data are complemented by results obtained with the Bogoliubov-de Gennes approximation. © 2014 IOP Publishing and Deutsche Physikalische Gesellschaft.
Source Title: New Journal of Physics
URI: https://scholarbank.nus.edu.sg/handle/10635/180770
ISSN: 1367-2630
DOI: 10.1088/1367-2630/16/1/013004
Rights: Attribution 4.0 International
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