Multiband Electronic Raman Scattering in Bilayer Superconductors

Physics – Condensed Matter

Scientific paper

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Previous version has been changed to include a more general discussion of the role of band structure, energy gap, and Raman ve

Scientific paper

10.1103/PhysRevB.54.12523

A theory of electronic Raman scattering in the presence of several energy bands crossing the Fermi surface is developed. The contributions to the light scattering cross section are calculated for each band and it is shown that the cross section can be written in terms of the sum of the single band contributions and a mixing term which only contributes to the fully symmetric channels $(A_{1g})$. Particular emphasis is placed on screening in bilayer superconductors. Since any charge fluctuation with long range character in real space is screened by the Coulomb interaction, the relevant fluctuations in a single layer case are induced between different parts of the Fermi surface. In a single band $d$-wave superconductor the scattering at energy transfer twice the maximum gap $\Delta_{max}$ is dominated by those parts of the Fermi surface where $\Delta^2({\bf k})$ is largest. As a consequence, the fully symmetric ($A_{1g}$) scattering is screened. In the case of a bilayer superconductor however, the charge transfer is possible between layers inside the unit cell. Therefore a formalism is considered which is valid for general band structure, superconducting energy gaps, and inter-layer hopping matrix elements. The spectra is calculated for La 2:1:4 and Y 1:2:3 as representative single and bilayer superconductors. The mixing term is found to be negligible and thus the response is well approximated by the sum of the contributions from the individual bands. The theory imposes strong constraints on both the magnitude and symmetry of the energy gap for the bilayer cuprates, and indicates that a nearly identical energy gap of $d_{x^{2}-y^{2}}$ symmetry provides a best fit to the data. However, the $A_{1g}$ part of the spectrum depends sensitively on many parameters.

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