A priori bounds for some infinitely renormalizable quadratic: IV. Elephant Eyes
Jeremy Kahn, Misha Lyubich
TL;DR
The work tackles MLC by deriving a priori bounds for a broad class of infinitely renormalizable quadratic-like maps with elephant eye combinatorics, including unbounded combinatorial limbs. It introduces uniform thin-thick decompositions for bordered Riemann surfaces and a detailed canonical-lamination framework to control geometric and combinatorial data under pullbacks. A central contribution is the Amplification Theorem, providing a dichotomy between amplification (dominance of vertical weight) and confinement (weight localized on short arcs), yielding bounded or controlled unbounded behavior. These tools culminate in local connectivity results for the Julia sets and, via puzzle arguments, MLC at the corresponding parameter values, with broader potential applications to degenerations near the Mandelbrot cusp. The methodology blends uniform-width estimates, Hubbard-tree dynamics, and a sophisticated weight-tracking scheme to tame infinite renormalization.
Abstract
In this paper we prove a priori bounds for an ``elephant eye'' combinatorics. Little $M$-copies specifying these combinatorics are allowed to converge to the cusp of the Mandelbrot set. To handle it, we develope a new geometric tool: uniform thin-thick decompositions for bordered Riemann surfaces.
