REVIEW 3 cited by
Minimal lectures on two-dimensional conformal field theory
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We provide a brief but self-contained review of two-dimensional conformal field theory, from the basic principles to some of the simplest models. From the representations of the Virasoro algebra on the one hand, and the state-field correspondence on the other hand, we deduce Ward identities and Belavin--Polyakov--Zamolodchikov equations for correlation functions. We then explain the principles of the conformal bootstrap method, and introduce conformal blocks. This allows us to define and solve minimal models and Liouville theory. In particular, we study their three- and four-point functions, and discuss their existence and uniqueness. In appendices, we introduce the free boson theory (with an arbitrary central charge), and the modular bootstrap in minimal models.
Forward citations
Cited by 3 Pith papers
-
Dynamical Entanglement Phase Transitions in Holographic CFTs
In large-central-charge holographic CFTs, post-quench mutual information organizes into six phases governed by conformal block dominance and D4 symmetry breaking to Z2 x Z2.
-
Characterizing gapped phases by smeared boundary conformal field theories: Duality in unusual ordering with spontaneously broken generalized symmetries
Gapped phases dual to massless RG flows in 2D CFTs exhibit unusual ordering via spontaneous breaking of non-group-like symmetries and are characterized using smeared boundary CFTs applied to smeared Ishibashi states.
-
Selection rules for RG flows of minimal models
A constructive enumeration of all local minimal-model CFTs, organized by Jones index, yields selection rules for RG flows that recover known results and predict new ones.
Discussion (0). Continue with ORCID to comment.