Separation of variables and correlation functions

Abstract: I will present new results in the separation of variables (SoV) program for integrable models. The SoV methods are expected to be very powerful but until recently have been barely developed beyond the simplest gl(2) examples. I will describe how to realize the SoV for any gl(N) spin chain and demonstrate how to solve the longstanding problem of deriving the scalar product measure in SoV. Using these results I will show how to compute a large class of correlation functions and overlaps in a compact determinant form. I will also demonstrate the power of SoV in 4d integrable CFT's such as the 'fishnet' theory and outline highly promising applications in computation of exact correlators in N=4 super Yang-Mills theory.

mathematical physics

Audience: researchers in the topic

Séminaire de physique mathématique IPhT