A Monte Carlo approach to the conformal bootstrap
Alessandro Laio, Uriel Luviano Valenzuela, and Marco Serone

TL;DR
This paper presents a stochastic Monte Carlo method to find approximate solutions to conformal bootstrap equations in various dimensions, identifying known theories and new regions of interest.
Contribution
It introduces a Monte Carlo approach guided by an action to explore solutions of bootstrap equations, complementing existing semi-definite programming methods.
Findings
Identifies minima corresponding to free theories and known models like Ising and Yang-Lee.
Discovers regions in parameter space aligned with extremality lines and generalized free theories.
Demonstrates the method's ability to find approximate solutions in multiple dimensions.
Abstract
We introduce an approach to find approximate numerical solutions of truncated bootstrap equations for Conformal Field Theories (CFTs) in arbitrary dimensions. The method is based on a stochastic search via a Metropolis algorithm guided by an action which is the logarithm of the truncated bootstrap equations for a single scalar field correlator. While numerical conformal bootstrap methods based on semi-definite programming put rigorous exclusion bounds on CFTs, this method looks for approximate solutions, which correspond to local minima of , when present, and can be even far from the extremality region. By this protocol we find that if no constraint on the operator scaling dimensions is imposed, has a single minimum, corresponding to the Free Theory. If we fix the external operator dimension, however, we encounter minima that can be studied with our approach. Imposing a…
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