TL;DR
PyCFTBoot provides an accessible interface for conformal bootstrap calculations, integrating symbolic and numerical tools to verify known results and explore fixed points across dimensions.
Contribution
It introduces a flexible, user-friendly wrapper for conformal bootstrap computations, leveraging Symengine and SDPB for symbolic and numerical processing.
Findings
Multi-correlator bootstrap identifies Wilson-Fisher fixed points in 3-4 dimensions.
PyCFTBoot simplifies conformal bootstrap calculations.
Verification of previous bootstrap results.
Abstract
We introduce PyCFTBoot, a wrapper designed to reduce the barrier to entry in conformal bootstrap calculations that require semidefinite programming. Symengine and SDPB are used for the most intensive symbolic and numerical steps respectively. After reviewing the built-in algorithms for conformal blocks, we explain how to use the code through a number of examples that verify past results. As an application, we show that the multi-correlator bootstrap still appears to single out the Wilson-Fisher fixed points as special theories in dimensions between 3 and 4 despite the recent proof that they violate unitarity.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
