Evidence for the Strongest Version of the 4d a-Theorem, via a-Maximization Along RG Flows
Edwin Barnes, Ken Intriligator, Brian Wecht, Jason Wright

TL;DR
This paper provides evidence supporting the strongest form of the 4d a-theorem by extending a-maximization to RG flows, proposing that RG flows are gradient flows of an a-function with a positive metric, and confirming this in perturbation theory.
Contribution
The authors extend a-maximization to RG flows away from fixed points, proposing a monotonic a-function and the gradient flow nature of RG flows, strengthening the evidence for the strongest 4d a-theorem.
Findings
a-maximization yields a monotonic a-function along RG flows
RG flows are gradient flows of the a-function with a positive metric
Perturbative RG flow metric matches previous computations by Osborn et al.
Abstract
In earlier work, we (KI and BW) gave a two line "almost proof" (for supersymmetric RG flows) of the weakest form of the conjectured 4d a-theorem, that a_{IR}<a_{UV}, using our result that the exact superconformal R-symmetry of 4d SCFTs maximizes a=3Tr R^3-Tr R. The proof was incomplete because of two identified loopholes: theories with accidental symmetries, and the fact that it's only a local maximum of \it{a}. Here we discuss and extend a proposal of Kutasov (which helps close the latter loophole) in which a-maximization is generalized away from the endpoints of the RG flow, with Lagrange multipliers that are conjectured to be identified with the running coupling constants. a-maximization then yields a monotonically decreasing "a-function" along the RG flow to the IR. As we discuss, this proposal in fact suggests the strongest version of the a-theorem: that 4d RG flows are gradient…
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.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
