Descending into the Modular Bootstrap

TL;DR

This paper introduces a machine-learning-inspired method to explore 2d conformal field theories by numerically solving the modular bootstrap equation, revealing new candidate theories and tighter spectral gap constraints.

Contribution

It develops a novel optimization approach with uncertainty estimation and a singular-value-based optimizer to identify potential 2d CFTs in unexplored central charge ranges.

Findings

Constructed candidate CFT partition functions for 1 < c < 8/7.

Provided evidence for a stricter spectral gap bound near c=1.

Demonstrated the effectiveness of the Sven optimizer over gradient descent.

Abstract

In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap equation using machine-learning-style optimization. The torus partition function of a 2d CFT is fixed by the spectrum of its primary operators and its chiral algebra, which we take to be the Virasoro algebra with $c>1$. We translate the requirement that this partition function is modular invariant into a loss function, which we then minimize to identify possible primary spectra. Our approach involves two technical innovations that facilitate finding reliable candidate CFTs. The first is a strategy to estimate the uncertainty associated with truncating the spectrum to the lowest dimension operators. The second is the use of a new singular-value-based optimizer (Sven) that is more effective than gradient descent at navigating the hierarchical structure of the loss landscape. We numerically construct candidate truncated CFT partition functions with central charges between 1 and $\frac{8}{7}$, a range devoid of known examples, and argue that these candidates likely come from a continuous space of modular bootstrap solutions. We also provide evidence for a more stringent constraint on the spectral gap near $c = 1$ than the existing bound of $\Delta_{\rm gap} \le \frac{c}{6} + \frac{1}{3}$.