REVIEW 5 cited by
6D (2,0) Bootstrap with soft-Actor-Critic
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 study numerically the 6D (2,0) superconformal bootstrap using the soft-Actor-Critic (SAC) algorithm as a stochastic optimizer. We focus on the four-point functions of scalar superconformal primaries in the energy-momentum multiplet. Starting from the supergravity limit, we perform searches for adiabatically varied central charges and derive two curves for a collection of 80 CFT data (70 of these data correspond to unprotected long multiplets and 10 to protected short multiplets). We conjecture that the two curves capture the A- and D-series (2,0) theories. Our results are competitive when compared to the existing bounds coming from standard numerical bootstrap methods, and data obtained using the OPE inversion formula. With this paper we are also releasing our Python implementation of the SAC algorithm, BootSTOP. The paper discusses the main functionality features of this package.
Forward citations
Cited by 5 Pith papers
-
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...
-
Descending into the Modular Bootstrap
Numerical search finds candidate modular-invariant spectra with integer degeneracies for 1 < c < 8/7 and hints at a stronger gap bound near c = 1.
-
Accurate boundary bootstrap for the three-dimensional O($N$) normal universality class
High-truncation eta-minimization bootstrap yields accurate boundary critical amplitudes for the 3d O(N) normal universality class, resolving prior Monte Carlo discrepancies and giving new Ising boundary data.
-
Machine Learning the 6d Supergravity Landscape
An autoencoder and two neural classifiers, trained only on anomaly Gram matrices, provide automated clustering, outlier detection, and consistency predictions for millions of 6d supergravity building blocks.
-
Chiral algebra correlators of the $6$d, $\mathcal{N}=(2,0)$ theory with a defect
A chiral-algebra bootstrap reproduces the defect two-point correlators of the 6d (2,0) theory using only bulk-channel data and predicts new defect-channel OPE coefficients.
Discussion (0). Continue with ORCID to comment.