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Operator Product Expansion Coefficients of the 3D Ising Criticality via Quantum Fuzzy Sphere
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abstract
Conformal field theory (CFT) is the key to various critical phenomena. So far, most of studies focus on the critical exponents of various universalities, corresponding to conformal dimensions of CFT primary fields. However, other important yet intricate data such as the operator product expansion (OPE) coefficients governing the fusion of two primary fields, is largely unexplored before, specifically in dimensions higher than 2D (or equivalently $1+1$D). Here, motivated by the recently-proposed fuzzy sphere regularization, we investigate the operator content of 3D Ising criticality starting from a microscopic description. We first outline the procedure of extracting OPE coefficients on the fuzzy sphere, and then compute 13 OPE coefficients of low-lying CFT primary fields. The obtained results are in agreement with the numerical conformal bootstrap data of 3D Ising CFT within a high accuracy. In addition, we also manage to obtain 4 OPE coefficients including $f_{T_{\mu\nu} T_{\rho\eta} \epsilon}$ that were not available before, which demonstrates the superior capabilities of our scheme. By expanding the horizon of the fuzzy sphere regularization from the state perspective to the operator perspective, we expect a lot of new physics ready for exploration.
Forward citations
Cited by 3 Pith papers
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Neural Spectral Bias and Conformal Correlators I: Introduction and Applications
Simple feed-forward neural networks trained on crossing symmetry plus a single anchor value reproduce CFT correlators to percent-level accuracy, and the authors conjecture this works because physical correlators are t...
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Chern-Simons-matter conformal field theory on fuzzy sphere: Confinement transition of Kalmeyer-Laughlin chiral spin liquid
A fuzzy-sphere exact diagonalization study shows the fIQH to bFQH transition is continuous, with emergent conformal symmetry and a single relevant singlet of scaling dimension Delta_S = 1.52(18).
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Neural Networks Reveal a Universal Bias in Conformal Correlators
Simple neural networks trained on crossing symmetry and one anchor point reproduce conformal correlators to within a few percent across many CFTs.
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