Bootstrapping Mixed Correlators in the Five Dimensional Critical O(N) Models

TL;DR

This paper uses conformal bootstrap to analyze five-dimensional $O(N)$ models, identifying scaling dimensions and critical $N_c$ where unitary behavior ceases, with results aligning with large $N$ expansion for large $N$.

Contribution

It introduces a bootstrap-based method to determine scaling dimensions and critical $N_c$ in 5D $O(N)$ models, revealing a higher-than-expected critical value.

Findings

Isolated regions consistent with large $N$ expansion for $N=500$

Critical $N_c$ for unitarity is greater than 100

Nonunitary interacting $O(N)$ CFTs likely exist below $N_c$

Abstract

We use the conformal bootstrap approach to explore $5D$ CFTs with $O(N)$ global symmetry, which contain $N$ scalars $\phi_i$ transforming as $O(N)$ vector. Specifically, we study multiple four-point correlators of the leading $O(N)$ vector $\phi_i$ and the $O(N)$ singlet $\sigma$. The crossing symmetry of the four-point functions and the unitarity condition provide nontrivial constraints on the scaling dimensions ($\Delta_\phi$, $\Delta_\sigma$) of $\phi_i$ and $\sigma$. With reasonable assumptions on the gaps between scaling dimensions of $\phi_i$ ($\sigma$) and the next $O(N)$ vector (singlet) scalar, we are able to isolate the scaling dimensions $(\Delta_\phi$, $\Delta_\sigma)$ in small islands. In particular, for large $N=500$, the isolated region is highly consistent with the result obtained from large $N$ expansion. We also study the interacting $O(N)$ CFTs for $1\leqslant N\leqslant100$. Isolated regions on $(\Delta_\phi,\Delta_\sigma)$ plane are obtained using conformal bootstrap program with lower order of derivatives $\Lambda$; however, they disappear after increasing $\Lambda$. We think these islands are corresponding to interacting but nonunitary $O(N)$ CFTs. Our results provide a lower bound on the critical value $N_c>100$, below which the interacting $O(N)$ CFTs turn into nonunitary. The critical value is unexpectedly large comparing with previous estimations.