Leading epsilon corrections to boundary anomalous dimensions and OPE coefficients in phi^3 BCFTs for Yang-Lee and S_{N+1} Potts models, plus higher-derivative generalizations.
Critical exponents of the 3d Ising and related models from Conformal Bootstrap
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abstract
The constraints of conformal bootstrap are applied to investigate a set of conformal field theories in various dimensions. The prescriptions can be applied to both unitary and non unitary theories allowing for the study of the spectrum of low-lying primary operators of the theory. We evaluate the lowest scaling dimensions of the local operators associated with the Yang-Lee edge singularity for $2 \le D \le 6$. Likewise we obtain the scaling dimensions of six scalars and four spinning operators for the 3d critical Ising model. Our findings are in agreement with existing results to a per mill precision and estimate several new exponents.
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A prototype successfully upgrades low-order extremal flow solutions to high numerical order for gap maximization in a simple spinning modular bootstrap test case.
A neural-network approach with dispersion relations handles infinite OPE towers in thermal conformal correlators without positivity.
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Boundary anomalous dimensions from BCFT: $\phi^{3}$ theories with a boundary and higher-derivative generalizations
Leading epsilon corrections to boundary anomalous dimensions and OPE coefficients in phi^3 BCFTs for Yang-Lee and S_{N+1} Potts models, plus higher-derivative generalizations.
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Efficient Conformal Block Evaluation with GoBlocks
GoBlocks delivers faster recursive evaluation of conformal blocks than prior packages, enabling mixed-correlator bootstrap optimizations for the 3D Ising model and O(N) vectors.
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$\mathcal{PT}$-symmetric Field Theories at Finite Temperature
A thermal normal-ordering scheme yields systematic epsilon-expansions for thermal observables in PT-symmetric cubic and quintic O(N) models, agreeing with exact 2D results from minimal models M(2,5) and M(3,8)_D and providing higher-d extrapolations.
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A prototype successfully upgrades low-order extremal flow solutions to high numerical order for gap maximization in a simple spinning modular bootstrap test case.
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A neural-network approach with dispersion relations handles infinite OPE towers in thermal conformal correlators without positivity.