Direct and inverse results for popular differences in trees of positive dimension

Abstract

We establish analogues for trees of results relating the density of a set ${E \subset \mathbb {N}}$, the density of its set of popular differences and the structure of E. To obtain our results, we formalize a correspondence principle of Furstenberg and Weiss which relates combinatorial data on a tree to the dynamics of a Markov process. Our main tools are Kneser-type inverse theorems for sets of return times in measure-preserving systems. In the ergodic setting, we use a recent result of the first author with Björklund and Shkredov and a stability-type extension (proved jointly with Shkredov); we also prove a new result for non-ergodic systems.

1 Introduction

In [FW03] Furstenberg and Weiss initiated the use of dynamical methods in the study of Ramsey theoretic questions for trees. They proved a Szemerédi-type theorem using a multiple recurrence result for a class of Markov processes (a purely combinatorial proof was later given by Pach, Solymosi and Tardos [PST12]). More precisely, they showed that finite replicas of the full binary tree could always be found in (infinite) trees of positive growth rate. It is then a natural question to quantify the abundance of finite configurations in a tree in relation to its size as measured by its upper Minkowski and Hausdorff dimensions.

To begin, we review the analogous question in the integer setting. Specifically, we consider the abundance of configurations in a subset $E \subset \mathbb {N}$ . Recall that the upper density and upper Banach density of E are

$$ \begin{align*} \overline{d}(E) = \limsup_{N \to \infty} \frac{|E \cap \{0,\ldots,N\}|}{N+1}, \quad d^{\ast}(E) = \limsup_{N-M \to \infty} \frac{|E \cap \{M,\ldots,N\}|}{N-M+1}. \end{align*} $$

The abundance of $2$ -term arithmetic progressions (2-APs) in E can be related to the density of E in the following way. Consider the sets of popular differences of E with respect to $\overline {d}$ and $d^{\ast }$ defined by

$$ \begin{align*} \overline{\Delta}_0(E) = \{n \in \mathbb{N} \colon \overline{d}(E \cap (E-n))>0\}, \quad \Delta_0^{\ast}(E) = \{n \in \mathbb{N} \colon d^{\ast}(E \cap (E-n))>0\}. \end{align*} $$

Furstenberg’s correspondence principle [Fur77] states that there exists a measure- preserving system $(X,\mathscr {B},\nu ,S)$ and $A \in \mathscr {B}$ with $\nu (A) = \overline {d}(E)$ such that for all integers $k \geqslant 1$ and $0=n_1,\ldots ,n_k \in \mathbb {N}$ ,

$$ \begin{align*} \overline{d}((E-n_1) \cap \cdots \cap (E-n_k)) \geqslant \nu(S^{-n_1}A \cap \cdots \cap S^{-n_k}A). \end{align*} $$

Taking $k=2$ , it follows that $\overline {\Delta }_0(E)$ contains

$$ \begin{align*} \mathcal{R}=\mathcal{R}(A) = \{n \in \mathbb{N} \colon \nu(A \cap S^{-n}A)>0\}, \end{align*} $$

the set of return times of A. Applying the mean ergodic theorem then gives

(1) $$ \begin{align} \underline{d}(\overline{\Delta}_0(E)) \geqslant \underline{d}(\mathcal{R}) \geqslant \lim_{N \to \infty} \frac{1}{N+1} \sum_{n=0}^{N}\frac{\nu(A \cap S^{-n}A)}{\nu(A)} \geqslant \nu(A) = \overline{d}(E), \end{align} $$

where the lower density $\underline {d}$ is defined for $E \subset \mathbb {N}$ by

$$ \begin{align*} \underline{d}(E) = \liminf_{N \to \infty} \frac{|E \cap \{0,\ldots,N\}|}{N+1}. \end{align*} $$

If in the above the upper density is replaced by the upper Banach density, then $\nu $ can further be chosen to be ergodic [Fur81, Proposition 3.9] (see [BHK05, Proposition 3.1] for an explicit proof).

Following Furstenberg and Weiss [FW03], we formulate a correspondence principle for arbitrary finite configurations in a tree and use it to obtain analogues of the inequality (1). We then analyze the case of equality in (1) and its analogues for trees using inverse theorems for the set of return times. In the ergodic situation we use a result of Björklund, the first author and Shkredov [BFS21] and a stability-type extension proved jointly with Shkredov in Appendix A, whereas in the general case we prove a slightly weaker statement (Theorem 5.1). Using these, we obtain inverse theorems for inequality (1): a tree for which equality holds must contain arbitrarily long ‘arithmetic progressions’ with a fixed common difference.

1.1 Main results

To describe our results, we first summarize the necessary definitions (see §2 for precise formulations). For clarity of exposition, in this introduction we restrict our attention to the case $r=2$ of our results and make corresponding simplifications to the notation.

Fix an integer $q \geqslant 2$ . In this paper, a tree can be visualized as a directed graph T with a distinguished vertex (the root) having no incoming edges, such that each vertex has between $1$ and q outgoing edges and each non-root vertex has exactly one incoming edge. (Technically, we work with the vertices of the graph with the partial order induced by directed paths.) The ‘size’ of T can be quantified by its upper Minkowski and Hausdorff dimensions $\overline {\dim }_MT$ and $\dim T$ , which are defined by an identification of such trees with closed subsets of $[0,1]$ .

1.1.1 Tree analogues of popular difference sets

A k-term arithmetic progression (k-AP) in $E \subset \mathbb {N}$ can be viewed as an affine map $\{0,\ldots ,k-1\} \to E$ . We consider ‘affine’ maps satisfying certain branching conditions from configurations C (‘finite trees’) to trees T. If there exists such a map with ‘common difference’ n taking the root of the configuration to $v \in T$ , we say that $v \in C_n = C_n(T)$ . The set $C_n$ corresponds to the set $E \cap (E-n) \cap \cdots \cap (E-(k-1)n)$ for k-APs in $E \subset \mathbb {N}$ . Using extensions of upper density and upper Banach density to subsets of trees, we define sets of ‘generic parameters’

$$ \begin{align*} \overline{G}(C) = \{n \in \mathbb{N} \colon \overline{d}(C_n)> 0 \}, \quad G^{\ast}(C) = \{n \in \mathbb{N} \colon d^{\ast}(C_n) > 0 \}. \end{align*} $$

We also introduce certain configurations F and D which are analogues of $2$ -APs, and their generic parameters can be interpreted as popular differences for trees. In particular, our first result is a version of (1).

Theorem A. (= Theorems 4.1 and 4.2 for $r=2$ )

For any tree T we have

$$ \begin{align*}\underline{d}(\overline{G}(F)) \geqslant \underline{d}(\overline{G}(D)) \geqslant \overline{\dim}_M T \quad \text{and} \quad \underline{d}(G^{\ast}(F)) \geqslant \underline{d}(G^{\ast}(D)) \geqslant \dim T.\end{align*} $$

1.1.2 Inverse theorems for sets of return times

Given the direct result Theorem A, we are interested in characterizing trees such that equality holds (or almost holds). To illustrate the ideas, we consider here the situation when equality is (almost) achieved in (1), which is the analogous question for subsets of $\mathbb {N}$ . Observe that the density of the set of return times of A is then close to the measure of A. It is natural to expect in this situation that the dynamics of A under S is rigid in some way, and this is indeed the case.

Let $(X,\mathscr {B},\nu ,S)$ be a measure-preserving system, and let A be a measurable set with $\nu (A)> 0$ and set of return times $\mathcal {R}$ . Using a theorem of Kneser we prove the following result.

Theorem B. (= Theorem 5.1)

If $\overline {d}(\mathcal {R}) = \nu (A)> 0$ , then there exists an integer $m \geqslant 1$ such that up to $\nu $ -null sets

$$ \begin{align*}X = \bigsqcup_{i=0}^{m-1} S^{-i} A. \end{align*} $$

Question 1.1. Does the assumption $\underline {d}(\mathcal {R}) = \nu (A)$ suffice to prove the conclusion of Theorem B?

If $\nu $ is ergodic, then Question 1.1 has an affirmative answer, and further there is an inverse result for cases of almost equality. The following theorem is an easy corollary of results by Björklund, the first author and Shkredov in [BFS21].

Theorem 1.2. (= Theorem 5.4)

If $(X,\mathscr {B},\nu ,S)$ is ergodic and

$$ \begin{align*} 0 < \underline{d}(\mathcal{R}) < \tfrac{3}{2} \nu(A), \end{align*} $$

then there exists an integer $m \geqslant 1$ such that $\mathcal {R} = m \mathbb {N}$ and $X = \bigsqcup _{i=0}^{m-1} S^{-i}( \bigcup _{j=0}^{\infty }S^{-jm} A )$ up to $\nu $ -null sets.

Remark 1.3. Example 1.2 in [BFS21] shows that for every $\beta> 1$ , there exists a non-ergodic measure-preserving system $(X,\mathscr {B},\nu ,S)$ and $A \in \mathscr {B}$ of arbitrarily small measure such that $\overline {d}(\mathcal {R}) \leqslant \beta \nu (A)$ and there is no $m \geqslant 1$ such that $\mathcal {R} = m\mathbb {N}$ .

1.1.3 Inverse results for popular difference sets

As a corollary of Theorem B and Furstenberg’s correspondence principle, we immediately obtain the following inverse-type result for (1).

Proposition 1.4. Assume that $E \subset \mathbb {N}$ satisfies $\overline {d}(\overline {\Delta }_0(E)) = \overline {d}(E)>0$ . Then there exists $m \geqslant 1$ such that $m \mathbb {N} \subset \overline {\Delta }_0(E)$ and $\overline {d}(\overline {\Delta }_0(E)) = \overline {d}(E) = m^{-1}$ . Moreover, for every $k \geqslant 2$

$$ \begin{align*} \overline{d}( E \cap (E-m) \cap \cdots \cap (E- (k-1)m) ) = \overline{d}(E). \end{align*} $$

If we consider $\Delta ^{\ast }_0(E)$ and $d^{\ast }(E)$ in place of $\overline {\Delta }_0(E)$ and $\overline {d}(E)$ , we can apply Theorem 1.2 to obtain the following inverse result.

Proposition 1.5. Let $1 \leqslant \beta < 3/2$ . Assume that $E \subset \mathbb {N}$ satisfies

$$ \begin{align*}0 < \underline{d}(\Delta^{\ast}_0(E)) = \beta \cdot d^{\ast}(E). \end{align*} $$

Then there exists $m \geqslant 1$ such that $m \mathbb {N} \subset \Delta ^{\ast }_0(E)$ . Moreover, for every $k \geqslant 2$ that satisfies $(1 - \beta ^{-1})k < 1$ , we have

$$ \begin{align*} d^{\ast}(E \cap (E-m) \cap \cdots \cap (E- (k-1)m))> 0. \end{align*} $$

1.1.4 Inverse results for $\overline {G}(F)$ and $G^{\ast }(F)$

Propositions 1.4 and 1.5 can be interpreted as saying that (almost) equality holds in (1) for a subset $E \subset \mathbb {N}$ only if E is ‘similar’ to the periodic set $m\mathbb {N}$ . In the tree setting, we prove analogous results.

For every $m \geqslant 1$ , define $T_{m\mathbb {N}}$ to be the tree such that $v \in T_{m\mathbb {N}}$ has q outgoing edges if the directed path from the root to v has length a multiple of m and one outgoing edge otherwise. The inequalities in Theorem A are equalities for $T_{m\mathbb {N}}$ (see §2.1.1).

For every $k \geqslant 1$ , define the configuration $V^{m,k}$ to be the first k levels of $T_{m\mathbb {N}}$ . The following two theorems are analogues of Propositions 1.4 and 1.5, respectively.

Theorem C. (= Theorem 6.1 for $r=2$ )

Let T be a tree. Assume that

$$ \begin{align*}\overline{d}(\overline{G}(F)) = \overline{\dim}_M T> 0. \end{align*} $$

Then there exists an integer $m \geqslant 1$ such that $\overline {\dim }_M T = m^{-1}$ , and $\overline {d}(V^{m,k})> 0$ for every $k \geqslant 1$ .

Theorem D. (= Theorem 6.2 for $r=2$ )

Let T be a tree. Assume that

$$ \begin{align*} \underline{d}(G^{\ast}(F)) = \dim T> 0 \quad \text{or} \quad \overline{d}(G^{\ast}(D)) = \dim T > 0. \end{align*} $$

Then there exists an integer $m \geqslant 1$ such that $\dim T = m^{-1}$ , and $d^{\ast }(V^{m,k})> 0$ for every $k \geqslant 1$ .

Remark 1.6. We show in §2.1.2 that Theorem D cannot be improved. Indeed, for every $\varepsilon> 0$ there exists a tree $T_{\varepsilon }$ such that

$$ \begin{align*} 0 < \dim T_{\varepsilon} \leqslant \underline{d}(G^{\ast}(F)) < (1+\varepsilon) \dim T_{\varepsilon} \end{align*} $$

and the configuration $V^{m,k}$ does not appear at all in $T_{\varepsilon }$ for some large k.

Our final result is another partial analogue of Proposition 1.5.

Theorem 1.7. (= Theorem 6.4 for $r=2$ )

Let T be a tree. Assume that there exists ${\beta < 3/2}$ such that

$$ \begin{align*} 0 < \underline{d}(G^{\ast}(F)) = \beta \cdot \dim T. \end{align*} $$

Then there exists $m \geqslant 1$ with $m \mathbb {N} \subset G^{\ast }(F)$ .

1.2 Organization of the paper

After describing the combinatorial and dynamical background (§2) and establishing Furstenberg–Weiss correspondence principles (§3), in §4 we prove lower bounds for the densities of popular differences for trees. We then use inverse theorems for sets of return times in measure-preserving systems (§5 and Appendix A) to prove inverse theorems for these lower bounds (§6).

2 Trees and Markov processes

Fix an integer $q \geqslant 2$ , and for $2 \leqslant r \leqslant q$ define $\Lambda _r = \{0,\ldots ,r-1\}$ and $\Lambda = \Lambda _q$ . We set $\mathbb {N} = \{0,1,\ldots \}$ .

2.1 Combinatorial setup

Let $\Lambda ^{\ast } = \bigcup _{n=0}^{\infty } \Lambda ^n$ be the set of finite words over $\Lambda $ , where $\Lambda ^0$ is the singleton comprising the empty word $\emptyset $ . Consider the partial order $\leqslant $ on $\Lambda ^{\ast }$ defined by $v \leqslant w$ if w is the concatenation $vu$ of v and some $u \in \Lambda ^{\ast }$ . A tree is then a non-empty subset $T \subset \Lambda ^{\ast }$ closed under predecessors and having no maximal elements with respect to $\leqslant $ . We refer to elements of T as vertices (using the natural graph-theoretic terminology), and write $l(v)=n$ if $v \in T(n) = T \cap \Lambda ^n$ . Every tree contains $\emptyset $ (the root), and for every $v \in T$ there is a tree $T^v = \{w \in \Lambda ^{\ast } \colon vw \in T\}$ .

Remark 2.1. Trees are combinatorial realizations of closed sets in $\Lambda ^{\mathbb {N}}$ , a symbolic analogue of $[0,1]$ . Given a tree T, the set

$$ \begin{align*}\{(a_i)_{i\geqslant 0} \in \Lambda^{\mathbb{N}} \colon (a_0,\ldots,a_n) \in T \text{ for all }n \in \mathbb{N}\}\end{align*} $$

is closed in $\Lambda ^{\mathbb {N}}$ (with the product of discrete topologies on $\Lambda $ ), and there is an inverse map sending a closed subset $A \subset \Lambda ^{\mathbb {N}}$ to the tree

$$ \begin{align*} \{v \in \Lambda^{\ast} \colon vw \in A \text{ for some }w \in \Lambda^{\mathbb{N}}\}. \end{align*} $$

This motivates several definitions we give in the following.

The (upper) Minkowski dimension of T is

$$ \begin{align*} \overline{\dim}_M T = \limsup_{N \to \infty} \frac{\log_q|T(N)|}{N}. \end{align*} $$

To define the Hausdorff dimension of a tree, we first define the analogue of an irredundant open cover for trees. A section of a tree T is a finite subset $\Pi \subset T$ such that $|\Pi \cap \{w \in T \colon w \leqslant v\}|=1$ for all but finitely many $v \in T$ . Define also $l(\Pi ) = \min \{l(v) \colon v \in \Pi \}$ . Then the Hausdorff dimension of T is

$$ \begin{align*} \dim T = \inf \bigg\{ \unicode{x3bb}>0 \colon \liminf_{N \to \infty} \inf_{\substack{l(\Pi)=N \\ \Pi \text{ section of } T}} \sum_{v \in \Pi} q^{-\unicode{x3bb} l(v)} < 1\bigg\}. \end{align*} $$

Example 2.2. Given $E \subset \mathbb {N}$ and $2 \leqslant r \leqslant q$ , define the tree

$$ \begin{align*} T_E^r = \{\emptyset\} \cup \bigcup_{i=0}^{\infty} \prod_{0 \leqslant j \leqslant i} \Gamma_j \quad \text{ where } \Gamma_j = \begin{cases} \Lambda & \text{ if } j \in E, \\ \Lambda_{r-1} & \text{ otherwise.} \end{cases} \end{align*} $$

A straightforward calculation shows that

$$ \begin{align*} \overline{\dim}_M T_E^r &= \limsup_{N \to \infty} \frac{\log_q { q^{|E \cap \{0,\ldots,N-1\}|} (r-1)^{|E^c \cap \{0,\ldots,N -1\}|}}}{N} \\ &= \overline{d}(E) + \log_q (r-1) (1 - \overline{d}(E)). \end{align*} $$

If E is a ‘periodic’ set (such as $m\mathbb {N}$ ), then $T_E^r$ is ‘self-similar’ and $\overline {\dim }_M T_E^r = \dim T_E^r$ .

Elements of $\Lambda ^{\ast }$ correspond to cylinder sets of $\Lambda ^{\mathbb {N}}$ . By the Carathéodory extension theorem, Borel probability measures on $\Lambda ^{\mathbb {N}}$ are in bijection with functions $\tau \colon \Lambda ^{\ast } \to [0,1]$ such that $\tau (\emptyset ) = 1$ and $\tau (v) = \sum _{a \in \Lambda } \tau (va)$ for all $v \in \Lambda ^{\ast }$ . We call such functions Markov trees, because the support $|\tau | = \{v \in \Lambda ^{\ast } \colon \tau (v)>0\}$ of such a function is a tree. The set of Markov trees is a closed subspace of the compact space $[0,1]^{\Lambda ^{\ast }}$ with metric $d(\tau _1,\tau _2) = \sum _{v \in \Lambda ^{\ast }} q^{-l(v)}|\tau _1(v)-\tau _2(v)|$ . By abuse of notation we denote it by $\mathcal {P}(\Lambda ^{\mathbb {N}})$ , because it is homeomorphic to the space of Borel probability measures on $\Lambda ^{\mathbb {N}}$ with the weak- ${}^{\ast }$ topology.

The dimension of a Markov tree [Fur70, Definition 7] is

$$ \begin{align*}\dim \tau = \liminf_{\substack{l(\Pi)\to\infty \\ \Pi \text{ section of } |\tau|}} \frac{-\sum_{v \in \Pi} \tau(v)\log_q \tau(v)}{\sum_{v \in \Pi}l(v)\tau(v)}.\end{align*} $$

Given a subset $V \subset T$ , we define its upper density

$$ \begin{align*} \overline{d}(V) = \limsup_{N \to \infty} \frac{1}{|T(N)|} \sum_{v \in T(N)} \frac{|V \cap \{w \in T \colon w \leqslant v\}|}{N+1} \end{align*} $$

and its upper Banach density

$$ \begin{align*}d^{\ast}(V) = \limsup_{N \to \infty} \sup_{\substack{|\tau| \subset T \\ v \in |\tau|}} \frac{1}{N+1} \sum_{l(w) \leqslant N} \frac{\tau(vw)}{\tau(v)}1_V(vw). \end{align*} $$

Remark 2.3. These definitions specialize to their integer counterparts in the degenerate case $q=1$ , justifying the notation. The inequality $d^{\ast }(V) \geqslant \overline {d}(V)$ also holds for our more general definition. To see this, observe that it is enough to construct Markov trees $\pi _N$ supported on T such that

$$ \begin{align*}\sum_{l(w) \leqslant N} \pi_N(w)1_V(w) &= \sum_{v \in T(N)} \frac{|V \cap \{w \in T \colon w \leqslant v\}|}{|T(N)|}\\ &= \sum_{l(w) \leqslant N} \frac{|\{v \in T(N) \colon w \leqslant v\}|}{|T(N)|}1_V(w)\end{align*} $$

(the last equality follows from reindexing the sum). However, this formula defines such a Markov tree on vertices w with $l(w) \leqslant N$ , and we can choose $\pi _N$ to be any consistent extension to the remaining vertices (cf. the proof of Theorem 3.4).

Example 2.4. If $V = V(E) = \{v \in T \colon l(v) \in E\}$ for $E \subset \mathbb {N}$ and T a tree, then ${\overline {d}(V) = \overline {d}(E)}$ and $d^{\ast }(V) = d^{\ast }(E)$ . Both equalities follow directly from the definitions. For example, for the second equality we observe that for any $\tau $ with $|\tau | \subset T$ and any $v \in |\tau |$ we have

$$ \begin{align*}\frac{1}{N+1}\sum_{l(w) \leqslant N} \frac{\tau(vw)}{\tau(v)}1_V(vw) = \frac{|E \cap \{l(v),\ldots, l(v)+N\}|}{N+1}.\end{align*} $$

We use the term configuration to refer to a non-empty finite subset $C \subset \Lambda ^{\ast }$ closed under predecessors (a finite tree). Terminology and notation defined previously for trees are used for configurations as appropriate without comment. A configuration C is non-branching if $|C(n)| \leqslant 1$ for all $n \in \mathbb {N}$ and branching otherwise.

By analogy with arithmetic progressions in $\mathbb {N}$ , we consider ‘affine embeddings’ of C in a tree T. More precisely, for a vertex $v \in T$ and $n \in \mathbb {N}$ we say $v \in C_n = C_n(T)$ if there exists a map $\iota \colon C \to T$ such that:

• • $\iota (\emptyset )=v$ ;

• • $\iota (w_1) \leqslant \iota (w_2)$ if $w_1 \leqslant w_2$ ( $\iota $ is a map of posets);

• • if w is the longest initial subword common to $w_1$ and $w_2$ , then $\iota (w)$ is the longest initial subword common to $\iota (w_1)$ and $\iota (w_2)$ ( $\iota $ is infimum-preserving);

• • $l(\iota (w)) = l(v)+nl(w)$ for all $w \in C$ ( $\iota $ is ‘affine’).

Equivalently, we say the configuration C appears at v (with parameter n). Observe that trivially every configuration appears at every vertex with parameter $0$ .

We are concerned with the following configurations (see Figure 1):

$$ \begin{align*}F^r = \{\emptyset\} \cup \Lambda_r \cup 0\Lambda_r, \quad D^{r,k} = \bigcup_{n=0}^k \Lambda_r^n, \quad V^{r,m,k} = \{v \in T_{m\mathbb{N}}^r \colon l(v) \leqslant k+1\}.\end{align*} $$

Figure 1 The configuration $F^2$ appears at the root of $T^2_{3\mathbb {N}}$ with parameter $n=3$ , whereas $v \notin F^2_n$ for any $n \geqslant 1$ .

For every configuration C and tree T we define the sets of generic parameters

$$ \begin{align*}\overline{G}(C) =\overline{G}(C,T) = \{n \in \mathbb{N} \colon \overline{d}(C_n)>0\}, \end{align*} $$

$$ \begin{align*} G^{\ast}(C) = G^{\ast}(C,T) = \{n \in \mathbb{N} \colon d^{\ast}(C_n)>0\}. \end{align*} $$

Remark 2.5. Note that $F^r$ appears at $v \in T_E^r$ with parameter n if and only if $D^{r,2}$ appears at v with parameter n if and only if $l(v), l(v)+n \in E$ . Therefore, ${\overline {G}(F^r,T^r_E) = \overline {G}(D^{r,2},T^r_E) = \overline {\Delta _0}(E)}$ and $G^{\ast }(F^r,T^r_E)= G^{\ast }(D^{r,2},T^r_E) = \Delta ^{\ast }_0(E)$ by Example 2.4. This is why the generic parameters of $F^r$ and $D^{r,2}$ are analogues of popular differences for trees.

2.1.1 Equality in Theorems 4.1 and 4.2

The tree $T^r_{m\mathbb {N}}$ achieves equality in Theorems 4.1 and 4.2. Indeed, by Example 2.2,

$$ \begin{align*} \overline{\dim}_M T_{m\mathbb{N}}^r = \frac{1}{m} + \log_q (r-1) \bigg(1 - \frac{1}{m}\bigg). \end{align*} $$

The self-similarity of $T_{m\mathbb {N}}^r$ implies that $\dim T_{m\mathbb {N}}^r = \overline {\dim }_M T_{m\mathbb {N}}^r$ . In addition, by Remark 2.5 it follows that

$$ \begin{align*} \overline{G}(F^r, T^r_{m\mathbb{N}}) = G^{\ast}(F^r,T_{m\mathbb{N}}^r) = m \mathbb{N} \quad \text{and} \quad \underline{d}(\overline{G}(F^r,T^r_{m\mathbb{N}})) = \underline{d}(G^{\ast}(F^r,T_{m\mathbb{N}}^r)) = m^{-1}. \end{align*} $$

Hence,

$$ \begin{align*} \underline{d}(\overline{G}(F^r,T_{m\mathbb{N}}^r)) = \frac{\overline{\dim}_M T_{m\mathbb{N}}^r - \log_q (r-1)}{1 - \log_q(r-1)}, \end{align*} $$

and

$$ \begin{align*} \underline{d}(G^{\ast}(F^r,T_{m\mathbb{N}}^r) ) = \frac{\dim T_{m\mathbb{N}}^r - \log_q (r-1)}{1 - \log_q(r-1)}. \end{align*} $$

2.1.2 Sharpness of Theorem 6.2

Next, we modify the construction of $T_{m\mathbb {N}}^r$ to obtain for every $\varepsilon> 0$ a tree $T_{\varepsilon }$ with

$$ \begin{align*} 0 < \underline{d}(G^{\ast}(F^r,T_{\varepsilon}) ) = \overline{d}(G^{\ast}(D^{r,2}, T_{\varepsilon})) < (1+\varepsilon) \frac{\dim T_{\varepsilon} - \log_q (r-1)}{1 - \log_q(r-1)} \end{align*} $$

such that there exists $k \geqslant 1$ with $V^{r,m,k}_1$ not appearing in $T_{\varepsilon }$ .

Let $T_{\varepsilon } = T_E^r$ , where $E = m \mathbb {N} \setminus mM \mathbb {N}$ for some positive integer $M> 1+\varepsilon ^{-1}$ . Then

$$ \begin{align*} \overline{d}(E) = \frac{1}{m}\bigg(1-\frac{1}{M}\bigg)\end{align*} $$

and $V^{r,m,mM+1}_1$ does not appear in $T_{\varepsilon }$ . By the self-similarity of $T_{\varepsilon }$ and Example 2.2, we have

$$ \begin{align*} \dim T_{\varepsilon} = \overline{\dim}_M T_{\varepsilon} = \frac{1}{m}\bigg(1-\frac{1}{M}\bigg)+ \log_q (r-1) \bigg(1 - \frac{1}{m}\bigg(1-\frac{1}{M}\bigg)\bigg) \end{align*} $$

and, hence,

$$ \begin{align*}\frac{\dim T_{\varepsilon} - \log_q(r-1)}{1-\log_q(r-1)} = \frac{1}{m} \bigg(1-\frac{1}{M} \bigg).\end{align*} $$

As $\Delta _0^{\ast }(E) = m\mathbb {N}$ , observe that by Remark 2.5 we have $\underline {d}(G^{\ast }(F^r, T_{\varepsilon })) = \overline {d}(G^{\ast }(D^{r,2}, T_{\varepsilon })) = m^{-1}$ . Therefore,

$$ \begin{align*} \underline{d}(G^{\ast}(F^r, T_{\varepsilon})) = \overline{d}(G^{\ast}(D^{r,2},T_{\varepsilon})) &= \frac{1}{1 - M^{-1}} \cdot \frac{\dim T_{\varepsilon} - \log_q (r-1)}{1 - \log_q(r-1)}\\ &< (1+ \varepsilon) \frac{\dim T_{\varepsilon} - \log_q (r-1)}{1 - \log_q(r-1)}. \end{align*} $$

2.2 Dynamical setup

Given a Markov tree $\tau $ and $v \in |\tau |$ , define the Markov tree $\tau ^v$ by $\tau ^v(w) = \tau (vw)/\tau (v)$ for every $w \in \Lambda ^{\ast }$ . Using this we define a Markov process $p \colon M \to \mathcal {P}(M)$ on the space $M = \Lambda \times \mathcal {P}(\Lambda ^{\mathbb {N}})$ by $p(a,\tau ) = \sum _{i \in \Lambda } \tau (i) \delta _{(i,\tau ^i)} \in \mathcal {P}(M)$ . Here $a \in \Lambda $ can be interpreted as labelling the root of $\tau \in \mathcal {P}(\Lambda ^{\mathbb {N}})$ with information about the past under the dynamics $\tau \mapsto \tau ^a$ . As p is continuous, it induces a Markov operator P on $C(M)$ (a positive contraction satisfying $P1=1$ ) defined by the formula $Pf(a, \tau ) = \sum _{i \in \Lambda } \tau (i)f(i, \tau ^i)$ . The pair $(M,p)$ is a CP-process.

Remark 2.6. For simplicity of notation, frequently we denote a labelled Markov tree by its underlying Markov tree. Similarly, we write $p_{\tau } = p(a,\tau )$ because the latter is independent of a. Further, a labelled Markov tree denoted by $\tau ^a$ is always assumed to have label a.

By a distribution we mean a Borel probability measure. A distribution $\nu $ on M is stationary for $(M,p)$ if $\int _M Pf \,d\nu = \int _M f\,d\nu $ for all continuous f. Note that if $\nu $ is stationary, then the above formula for P extends to a well-defined operator on $L^p(M,\nu )$ for $1 \leqslant p \leqslant \infty $ , and by Jensen’s inequality this extension is a Markov operator. The set of stationary distributions for $(M,p)$ is a non-empty compact convex subset of $\mathcal{P}(M)$ , and its extremal points are called ergodic distributions.

For $i \in \Lambda $ , define the set $B_i = \{(a,\tau ) \in M \colon a = i\}$ of Markov trees labelled by i. The sets $B_i$ are clopen and partition M. Define also for $2 \leqslant r \leqslant q$ the set $A_r = \{\tau \in M \colon |\{i \colon p_{\tau }(B_i)>0\}| \geqslant r\}$ of Markov trees $\tau $ such that there are at least r vertices in $|\tau |(1)$ . Observe that $A_r$ is open and dense in M and, hence, is not closed for $r>1$ .

Define on M the information function

$$ \begin{align*}H(\tau) = -\sum_{i \in \Lambda} p_{\tau}(B_i) \log_q p_{\tau}(B_i) = -\sum_{i \in \Lambda} \tau(i)\log_q \tau(i),\end{align*} $$

where $0 \log _q 0 = 0$ by convention. The entropy of a stationary distribution $\nu $ is then $H(\nu ) = \int _M H\,d\nu $ . Note that $0 \leqslant H(\tau ) \leqslant \log _q | |\tau |(1)|$ .

Proposition 2.7. If $\nu $ is a stationary distribution for $(M,p)$ , then

$$ \begin{align*} \nu(A_r) \geqslant \frac{H(\nu)-\log_q(r-1)}{1-\log_q(r-1)}. \end{align*} $$

Proof. Using the above bounds on $H(\tau )$ and the definition of $A_r$ ,

$$ \begin{align*} H(\nu) = \int_{A_r} H\,d\nu + \int_{M \setminus A_r} H\,d\nu \leqslant \nu(A_r)+(1-\nu(A_r))\log_q(r-1). \end{align*} $$

Rearranging gives the proposition.

2.3 Endomorphic extension

It will be necessary to work with an extension of the CP-process $(M,p)$ , following [FW03].

Let $\widetilde {M} = \{\widetilde {\tau }=(\tau _i)_{i \leqslant 0} \in M^{\mathbb {Z}^{\leqslant 0}} \colon p_{\tau _i}(\{\tau _{i+1}\})>0 \text { for all }i<0\}$ . By abuse of notation, we denote by p the natural lift of $p \colon M \to \mathcal {P}(M)$ to a continuous function $\widetilde {M} \to \mathcal {P}(\widetilde {M})$ . Explicitly, $p_{\tilde {\tau }} = \sum _{a \in \Lambda } \tau _0(a) \delta _{\tilde {\tau }^{a}}$ , where $(\tilde {\tau }^a)_i = \tau _{i+1}$ for $i < 0$ and $(\tilde {\tau }^a)_0 = \tau _0^a$ . We also denote by P the corresponding Markov operator on $C(\widetilde {M})$ . The pair $(\widetilde {M},p)$ is said to be an endomorphic extension of $(M,p)$ .

A stationary distribution $\nu $ on M induces a stationary distribution $\widetilde {\nu }$ on $\widetilde {M}$ , and $\tilde{\nu} $ is invariant under the right shift $S \colon (\tau _i)_{i \leqslant 0} \mapsto (\tau _{i-1})_{i \leqslant 0}$ by construction [Ho14, Definition 6.3, Remark 6.4, and Lemma 6.8]. The Koopman operator of S therefore acts on ${\mathscr {H} = L^2(\widetilde {M},\mathscr {B},\widetilde {\nu })}$ , where $\mathscr {B}$ is the Borel $\sigma $ -algebra on $\widetilde {M}$ . As $p_{\widetilde {\tau }}(\{\widetilde {\omega }\})>0$ implies $S(\widetilde {\omega })=\widetilde {\tau }$ , a straightforward calculation gives the following result.

Lemma 2.8. For any $f,g \in \mathscr {H}$ we have $P(f Sg) = g Pf$ .

Integrating with respect to $\widetilde {\nu }$ shows that P and S are adjoint operators on $\mathscr {H}$ , and taking $f=1$ gives the formula $PS=I$ . It follows that $S^nP^n$ is the orthogonal projection from $\mathscr {H}$ onto the closed subspace $S^n\mathscr {H} = L^2(\widetilde {M},S^{-n}\mathscr {B},\nu )$ .

If $f =Sf' \in S\mathscr {H}$ , then $SPf=SPSf'=Sf'=f$ , so $SP=I$ on $S\mathscr {H}$ . Define $\mathscr {H}_{\infty } = \bigcap _{n \geqslant 1} S^n\mathscr {H} = L^2(\widetilde {M}, \mathscr {B}_{\infty },\nu )$ , where $\mathscr {B}_{\infty } = \bigcap _{n \geqslant 1} S^{-n}\mathscr {B}$ . For $f \in \mathscr {H}_{\infty }$ we have $Sf \in \mathscr {H}_{\infty }$ and $Pf \in \mathscr {H}_{\infty }$ (using $PS=I$ ), giving the following result.

Lemma 2.9. The operators P and S restrict to mutually inverse operators $\mathscr {H}_{\infty } \to \mathscr {H}_{\infty }$ .

Denote the orthogonal projection of $f \in \mathscr {H}$ onto $\mathscr {H}_{\infty }$ by $\overline {f}$ .

Proposition 2.10. For $f \in \mathscr {H}$ , $\|P^nf - P^n\overline {f}\|_2 \to 0$ .

Proof. As $\widetilde {\nu }$ is S-invariant, it follows from Lemma 2.9 that

$$ \begin{align*} \|P^nf - P^n\overline{f}\|_2 = \|S^nP^nf-S^nP^n\overline{f}\|_2 =\|S^nP^nf - \overline{f}\|_2 \to 0 \end{align*} $$

because $\|E(f \mid S^{-n}\mathscr {B} ) - E(f \mid \bigcap _{i \geqslant 1} S^{-i}\mathscr {B})\|_2 \to 0$ [EW11, Theorem 5.8].

By composing H with the projection $\widetilde {M}\to M$ onto the zeroth coordinate, the information function H is defined on $\widetilde {M}$ and, hence, the entropy of a stationary distribution for $(\widetilde {M},p)$ is defined as for $(M,p)$ .

3 The Furstenberg–Weiss correspondence principle

In [FW03] Furstenberg and Weiss associated to a tree of positive upper Minkowski dimension a stationary distribution for the CP-process $(M,p)$ , and showed that the appearance of $D^{2,k}_n$ could be deduced from the positivity of quantities defined on the dynamical system. In this section, we extend their construction to arbitrary configurations, and prove an analogous correspondence principle based on [Fur70] for trees of positive Hausdorff dimension.

3.1 Construction of configuration-detecting functions

Given a configuration C and an integer $n \geqslant 1$ , we say that a function $f \colon M \to [0,1]$ is $C_n$ -detecting if $f(\tau )>0$ if and only if C appears at the root of $|\tau |$ with parameter n. In preparation for proving correspondence principles, we construct recursively several families of configuration-detecting functions.

We first construct a set of configuration-detecting functions $\varphi _{C_n}$ . For the simplest configuration $\{\emptyset \}$ , we can take $\varphi _{\{\emptyset \}_n}=1$ for all $n \geqslant 1$ . Given $I \subset \Lambda $ such that $|I|=|C(1)|$ and a bijection $\beta \colon I \to C(1)$ , the positivity of $\prod _{i \in I} P(1_{B_i}P^{n-1}\varphi _{C_n^{\beta (i)}})$ at $\tau \in M$ is equivalent to the appearance of C at the root of $|\tau |$ with parameter n such that $\beta (i) \in C(1)$ is mapped to $iv \in |\tau |$ for some $v \in \Lambda ^{n-1}$ . Summing over all choices of I and $\beta $ , we define $\varphi _{C_n}$ by the recursive formula

$$ \begin{align*} \varphi_{C_n} = \sum_{\substack{I \subset \Lambda \\ |I|=|C(1)|}} \sum_{\beta \colon I \xrightarrow{\sim} C(1)} \prod_{i \in I} P(1_{B_i}P^{n-1}\varphi_{C_n^{\beta(i)}}). \end{align*} $$

Remark 3.1. Alternatively we could sum over all injections $\gamma :C(1) \to \Lambda $ and define $\varphi _{C_n}$  by

$$ \begin{align*} \varphi_{C_n} = \sum_{\gamma} \prod_{i \in C(1)} P(1_{B_{\gamma(i)}}P^{n-1}\varphi_{C_n^{i}}). \end{align*} $$

We also have $0 \leqslant \varphi _{C_n} \leqslant 1$ . Indeed, because $\varphi _{\{\emptyset \}_n}=1$ and P is positive

$$ \begin{align*} 0 \leqslant \varphi_{C_n} \leqslant \sum_{\substack{I \subset \Lambda \\ |I|=|C(1)|}} \sum_{\beta \colon I \xrightarrow{\sim} C(1)} \prod_{i \in I} P1_{B_i} \leqslant \bigg(\sum_{i \in \Lambda}P1_{B_i}\bigg)^{|C(1)|} = 1. \end{align*} $$

Starting instead with $\phi _{D_n^{r,1}} = 1_{A_r}$ and $\phi _{C_n} = 1$ for non-branching configurations C, we can adapt the above recursion to construct an alternative family of configuration-detecting functions $\phi _{C_n} \geqslant \varphi _{C_n}$ more suitable for computations. Let $C(1)' = \{v \in C(1) \colon C^v \text { is} \text {branching}\}$ . We define $\phi _{C_n}$ recursively by the formula

$$ \begin{align*}\phi_{C_n} = 1_{A_{|C(1)|}} \sum_{\substack{I \subset \Lambda \\ |I|=|C(1)'|}} \sum_{\beta \colon I \xrightarrow{\sim} C(1)'} \prod_{i \in I} P(1_{B_i}P^{n-1}\phi_{C_n^{\beta(i)}}).\end{align*} $$

Note that $\varphi _{D_n^{r,1}} \leqslant 1_{A_r} = \phi _{D_n^{r,1}}$ . Similarly, we have $0 \leqslant \phi _{C_n} \leqslant 1$ .

As the $B_i$ are clopen and P takes continuous functions to continuous functions, the $\varphi _{C_n}$ are continuous. However, in general the $\phi _{C_n}$ are not continuous because $A_r$ is not clopen for $r>1$ .

If C is a configuration such that the configurations $C^v$ are all ‘isomorphic’ for $v \in C(1)$ , the above recursion can be simplified by omitting the sum over bijections $\beta $ . For integers $2 \leqslant r \leqslant q$ and $n \geqslant 1$ , define (nonlinear) operators $R_{r,n}$ on $L^{\infty }(M)$ by

$$ \begin{align*}R_{r,n}f = \sum_{\substack{I \subset \Lambda \\ |I|=r}} \prod_{i \in I} P(1_{B_i}P^{n-1}f).\end{align*} $$

If f detects $C^v_n$ for (all) $v \in C(1)$ and $|C(1)|=r$ , then $R_{r,n}f$ detects $C_n$ . Denote by $\phi _{C_n}'$ the $C_n$ -detecting function obtained by applying a sequence of the above operators to the appropriate $1_{A_r}$ , and observe that $\phi _{C_n}=c\phi _{C_n}'$ for some integer $c \geqslant 1$ .

Example 3.2. For the configuration $F^r$ , we have $|C(1)|=r$ and $|C(1)'|=1$ . There is always a unique bijection $I \to C(1)'$ , so linearity of P gives

$$ \begin{align*} \phi_{F^r_n} = 1_{A_r} \sum_{i \in \Lambda} P(1_{B_i}P^{n-1}1_{A_r}) = 1_{A_r}P^n1_{A_r}\end{align*} $$

because $1=\sum _{i \in \Lambda }1_{B_i}$ .

If $C(1)=C(1)'$ , the factor $1_{A_{|C(1)|}}$ is redundant in the definition of $\phi _{C_n}$ as the function in the sum is already supported on a subset of $A_{|C(1)|}$ . For example,

$$ \begin{align*}\phi_{D^{r,2}_n} = \sum_{\substack{I \subset \Lambda \\ |I|=r}} \sum_{\beta \colon I \xrightarrow{\sim} \Lambda_r} \prod_{i \in I} P(1_{B_i}P^{n-1}1_{A_r}) = r! \sum_{\substack{I \subset \Lambda \\ |I|=r}} \prod_{i \in I} P(1_{B_i}P^{n-1}1_{A_r} ) = r!\phi_{D^{r,2}_n}'.\end{align*} $$

The following lemma is used in the proofs of the correspondence principles to account for the lack of continuity of $\phi _{C_n}$ .

Lemma 3.3. If $(\nu _k)_{k \geqslant 1}$ is a sequence of distributions on M converging to $\nu $ in the weak- ${}^{\ast }$ topology, then for every configuration C and integer $n \geqslant 1$

$$ \begin{align*}\limsup_{k \to \infty} \int_M\phi_{C_n}\,d\nu_k \geqslant \int_M \phi_{C_n}\,d\nu.\end{align*} $$

Proof. Define for $\delta \in [0,1]$ open sets $A_r^{\delta } = \{\tau \in M \colon |\{i \colon p_{\tau }(B_i)>\delta \}|\geqslant r\} \subset A_r$ , and let $\phi _{C_n}^{\delta }$ be the function obtained by replacing $1_{A_r}$ with $1_{A_r^{\delta }}$ in the recursive construction of $\phi _{C_n}$ . Observe that $\delta \leqslant \delta '$ implies $\phi _{C_n}^{\delta } \geqslant \phi _{C_n}^{\delta '}$ by the positivity of P. Then the monotone function $\alpha \colon \delta \mapsto \int _M \phi _{C_n}^{\delta }\,d\nu $ has countably many discontinuities, so we can choose a sequence $\delta _j \to 0$ such that $\alpha $ is continuous at $\delta _j$ for all j.

We claim $\int _M \phi _{C_n}^{\delta }\,d\nu _k \to \int _M \phi _{C_n}^{\delta }\,d\nu = \alpha (\delta )$ if $\alpha $ is continuous at $\delta $ . If $\delta < \delta '$ , the closed sets $(A_r^{\delta })^c$ and $\overline {A_r^{\delta '}} = \{\tau \in M \colon |\{i \colon p_{\tau }(B_i) \geqslant \delta '\}| \geqslant r\}$ are disjoint since ${\overline {A_r^{\delta '}} \subset A_r^{\delta }}$ . By Urysohn’s lemma, there are continuous functions $h_r$ such that $1_{A_r^{\delta '}} \leqslant h_r \leqslant 1_{A_r^{\delta }}$ . Defining $h_{C_n}$ to be the function obtained by repeating the construction of $\phi _{C_n}$ with $h_r$ in place of $1_{A_r}$ , it follows that $\phi _{C_n}^{\delta '} \leqslant h_{C_n} \leqslant \phi _{C_n}^{\delta }$ . As $h_{C_n}$ is continuous,

$$ \begin{align*} \liminf_{k \to \infty} \int_M \phi_{C_n}^{\delta} \,d\nu_k & \geqslant \liminf_{k \to \infty} \int_M h_{C_n}\,d\nu_k = \int_M h_{C_n}\,d\nu \geqslant \int_M \phi_{C_n}^{\delta'}\,d\nu = \alpha(\delta'). \end{align*} $$

Continuity of $\alpha $ at $\delta $ implies $\liminf _{k \to \infty } \int _M \phi _{C_n}^{\delta } \,d\nu _k \geqslant \alpha (\delta )$ , and a similar argument with $\delta '<\delta $ proves the claim. Hence,

$$ \begin{align*} \limsup_{k \to \infty} \int_M \phi_{C_n} \,d\nu_k &\geqslant \lim_{k \to \infty} \int_M \phi_{C_n}^{\delta_j}\,d\nu_k = \int_M \phi_{C_n}^{\delta_j}\,d\nu \xrightarrow{j \to \infty} \int_M \phi_{C_n}\,d\nu \end{align*} $$

by the monotone convergence theorem.

3.2 Correspondence principle for upper density

Theorem 3.4. For every tree T with $\overline {\dim }_M T>0$ , the CP-process $(M,p)$ has a stationary distribution $\mu $ such that $H(\mu )=\overline {\dim }_M T$ ,

(2) $$ \begin{align} \mu(A_r) \geqslant \frac{\overline{\dim}_MT - \log_q(r-1)}{1-\log_q(r-1)}, \end{align} $$

and for every configuration C and every integer $n \geqslant 1$ ,

(3) $$ \begin{align} \overline{d}(C_n) \geqslant \int_M \phi_{C_n} \,d \mu. \end{align} $$

Proof. Let $(L_k)_{k\geqslant 1}$ be an increasing sequence such that

$$ \begin{align*}\overline{\dim}_M T = \lim_{k \to \infty} \frac{\log_q |T(L_k+1)|}{L_k+1}.\end{align*} $$

Fix an arbitrary label $a \in \Lambda $ , and for each $k \geqslant 1$ let $\pi _k$ be any Markov tree labelled by a such that $\pi _k(v) = |T(L_k)|^{-1}$ for all $v \in T(L_k)$ (note that this condition determines $\pi _k$ on vertices of level at most $L_k$ ). Then any weak- ${}^{\ast }$ limit of the distributions

$$ \begin{align*}\mu_k = \frac{1}{L_k+1} \sum_{i=0}^{L_k} P^i\delta_{\pi_k} = \frac{1}{L_k+1} \sum_{l(v)\leqslant L_k} \pi_k(v) \delta_{\pi^v_k}\end{align*} $$

is stationary, and we choose $\mu $ to be such a limit.

As $H(x)$ is continuous and $\pi _k(v) = \sum _{a \in \Lambda } \pi _k(va)$ ,

(4) $$ \begin{align} H(\mu) &= \lim_{k \to \infty} \int_M H \,d\mu_{k} \nonumber \\ \nonumber &= -\lim_{k \to \infty} \frac{1}{L_k+1} \sum_{l(v) \leqslant L_k} \pi_k(v) \sum_{a \in \Lambda} \frac{\pi_k(va)}{\pi_k(v)} \log_q \frac{\pi_k(va)}{\pi_k(v)} \\ \nonumber &= -\lim_{k \to \infty} \frac{1}{L_k+1} \sum_{l(v) \leqslant L_k} \sum_{a \in \Lambda} \pi_k(va)\log_q \pi_k(va) - \pi_k(va)\log_q \pi_k(v) \\ &= -\lim_{k \to \infty} \frac{1}{L_k+1} \sum_{l(v)=L_k} \sum_{a \in \Lambda} \pi_k(va)\log_q \pi_k(va). \end{align} $$

Recall that for every $v \in |\pi _k|$ we have the bounds

(5) $$ \begin{align} -\pi_k(v)\log_q\pi_k(v) \leqslant -\sum_{a \in \Lambda} \pi_k(va)\log_q\pi_k(va) \leqslant -\pi_k(v) \log_q \frac{\pi_k(v)}{q}. \end{align} $$

As $-\sum _{l(v)=L_k} \pi _k(v) \log _q \pi _k(v) = \log _q|T(L_k)|$ by the definition of $\pi _k$ , summing the inequalities (5) over $v \in L_k$ and noting $\sum _{l(v)=L_k}\pi _k(v)=1$ gives

$$ \begin{align*}H(\mu)=\int_X H\,d\mu = \lim_{k \to \infty} \frac{\log_q|T(L_k)|}{L_k+1} = \lim_{k \to \infty} \frac{\log_q|T(L_k+1)|}{L_k+1} = \overline{\dim}_MT, \end{align*} $$

where the third equality follows from the bounds

$$ \begin{align*}q^{-1}|T(L_k+1)| \leqslant |T(L_k)| \leqslant |T(L_k+1)|.\end{align*} $$

Proposition 2.7 immediately gives the inequality (2).

To prove the inequality (3), applying a change of summation variable and using the definitions of $\pi _k$ and $\phi _{C_n}$ gives

$$ \begin{align*} \overline{d}(C_n) &\geqslant \limsup_{k \to \infty} \frac{1}{|T(L_k)|}\sum_{v \in T(L_k)} \frac{|C_n \cap \{w \in T \colon w \leqslant v\}|}{L_k+1} \\&= \limsup_{k \to \infty} \frac{1}{L_k+1} \sum_{l(w) \leqslant L_k} \pi_k(w) 1_{C_n}(w) \\&\geqslant \limsup_{k \to \infty} \frac{1}{L_k+1} \sum_{l(w) \leqslant L_k} \pi_k(w) \phi_{C_n}(\pi_k^w) \\&= \limsup_{k \to \infty} \int_M \phi_{C_n}\,d\mu_k. \end{align*} $$

The conclusion follows from Lemma 3.3.

3.3 Correspondence principle for upper Banach density

Theorem 3.5. If $\dim T>0$ , for every $\epsilon>0$ there exists an ergodic stationary distribution $\eta = \eta _{\epsilon }$ for the CP-process $(M,p)$ such that $H(\eta ) \geqslant \dim T-\epsilon $ ,

(6) $$ \begin{align} \eta(A_r) \geqslant \frac{\dim T-\epsilon-\log_q(r-1)}{1-\log_q(r-1)}, \end{align} $$

and for every configuration C and integer $n \geqslant 1$ ,

(7) $$ \begin{align} d^{\ast}(C_n) \geqslant \int_M \phi_{C_n}\,d\eta. \end{align} $$

Proof. For any $\epsilon>0$ , by Frostman’s lemma (see [Ma95, Theorem 8.8] and [Ho14, Theorem 3.12]) there exists $\theta \in M$ such that $|\theta | \subset T$ and $\dim \theta \geqslant \dim T - \epsilon $ . Let $(M_k)_{k\geqslant 1}$ be an increasing sequence such that the distributions

$$ \begin{align*}\eta^{\prime}_k = \frac{1}{M_k+1} \sum_{i=0}^{M_k} P^i\delta_{\theta} = \frac{1}{M_k+1}\sum_{l(v) \leqslant M_k} \theta(v)\delta_{\theta^v} \end{align*} $$

converge to a distribution $\eta '$ in the weak- ${}^{\ast }$ topology.

Lemma 3.6. [Fur70, Lemma 4]

We have $H(\eta ') \geqslant \dim \theta $ .

Proof. As in the calculation (4) we have

$$ \begin{align*} H(\eta') &= -\lim_{k \to \infty} \frac{1}{M_k+1}\sum_{l(v)=M_k+1} \theta(v)\log_q\theta(v) \\&= \lim_{k \to \infty}\frac{-\!\sum_{v \in \Pi_k}\theta(v)\log_q \theta(v)}{\sum_{v \in \Pi_k} l(v)\theta(v)} \geqslant \dim \theta , \end{align*} $$

where $\Pi _k$ is the section $|\theta |(M_k+1) = \{v \in |\theta | \colon l(v)=M_k+1\}$ .

The support of $\eta '$ is contained in the compact set $D(\theta ) = \overline {\{\theta ^v \colon v \in |\theta |\}}$ , and by Choquet’s theorem [Ph01, Ch. 3] there exists an ergodic distribution $\eta $ supported on $D(\theta )$ such that $H(\eta ) \geqslant H(\eta ') \geqslant \dim \theta \geqslant \dim T - \epsilon $ . The inequality (6) immediately follows from Proposition 2.7.

As $\eta $ is ergodic, the mean ergodic theorem for contractions [EFHN15, Theorem 8.6] implies

$$ \begin{align*}\frac{1}{N+1} \sum_{i=0}^N P^if \to \int_M f\,d\eta\end{align*} $$

in $L^2(M,\eta )$ for $f \in L^2(M,\eta )$ . By diagonalization there exists an increasing sequence $(N_k)_{k\geqslant 1}$ and $\tau \in D(\theta )$ such that

(8) $$ \begin{align} \frac{1}{N_k+1} \sum_{i=0}^{N_k} P^if(\tau) \to \int_M f\,d\eta \end{align} $$

for all f in a countable set of continuous functions. Taking this set to be dense in $C(M)$ under the uniform norm, the limit (8) holds for all continuous functions. Letting $v_k \in |\theta |$ be a sequence of vertices such that $\theta ^{v_k} \to \tau $ , and passing to a subsequence of $(v_k)$ if necessary, it follows that the sequence of measures $\eta _k$ defined by

$$ \begin{align*}\eta_k = \frac{1}{N_k+1}\sum_{i=0}^{N_k}P^i\delta_{\theta^{v_k}} = \frac{1}{N_k+1}\sum_{l(w)\leqslant N_k}\theta^{v_k}(w)\delta_{\theta^{v_kw}}\end{align*} $$

converges weakly to $\eta $ . For $\epsilon <\dim T$ it follows that

$$ \begin{align*} d^{\ast}(C_n) &\geqslant \limsup_{k \to \infty} \frac{1}{N_k+1} \sum_{l(w) \leqslant N_k} \theta^{v_k}(w)1_{C_n}(v_k w) \\ &\geqslant \limsup_{k \to \infty} \frac{1}{N_k+1} \sum_{l(w) \leqslant N_k} \theta^{v_k}(w)\phi_{C_n}(\theta^{v_kw}) \\ &= \limsup_{k \to \infty} \int_M \phi_{C_n} \,d\eta_k \end{align*} $$

and the inequality (7) follows from Lemma 3.3.

Remark 3.7. Composing the projection $(\tau _i)_{i \leqslant 0} \mapsto \tau _0$ with a $C_n$ -detecting function gives a map $\widetilde {M} \to [0,1]$ which is positive at $(\tau _i)_{i \leqslant 0}$ if and only if C appears at the root of $|\tau _0|$ with parameter n. The recursive constructions of configuration-detecting functions in §3.1 can be used to construct their lifts using the abuses of notation $B_i = \{\widetilde {\tau } \in M \colon \tau _0 \in B_i\}$ and $A_r = \{\widetilde {\tau } \in \widetilde {M} \colon |\{i \colon p_{\widetilde {\tau }}(B_i)>0\}| \geqslant r\}$ . Observe that the inequalities (2), (3), (6) and (7) are still valid when the distributions $\mu $ and $\eta _{\epsilon }$ and the configuration detecting functions $\phi _{C_n}$ are replaced with their lifts on $\widetilde {M}$ . In the remainder of the paper, we work only with $(\widetilde {M},p)$ and use Theorems 3.4 and 3.5 for the endomorphic extension without comment.

4 Proof of direct theorems

Using the correspondence principles of §3, we bound from below the densities of the sets of popular differences for trees. We first prove such a result for the generic parameters of the configuration $F^r$ , because the proof is relatively simple but contains the main ideas.

Theorem 4.1. Let T be a tree. For $2 \leqslant r \leqslant q$ we have

$$ \begin{align*}\underline{d}(\overline{G}(F^r)) \geqslant \frac{\overline{\dim}_MT - \log_q(r-1)}{1-\log_q(r-1)} \quad\mbox{and}\quad \underline{d}(G^*(F^r)) \geqslant \frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)}.\end{align*} $$

Proof. As P and S are adjoint, Theorem 3.4 gives

$$ \begin{align*}\overline{d}(F_n^r) \geqslant \int_{\widetilde{M}} \phi_{F_n^r}\,d\widetilde{\mu} = \int_{\widetilde{M}} 1_{A_r} P^n 1_{A_r} \,d \widetilde{\mu} = \int_{\widetilde{M}} 1_{A_r}S^n1_{A_r}\,d\widetilde{\mu} = \widetilde{\mu}(A_r \cap S^{-n}A_r). \end{align*} $$

Hence, $\overline {G}(F^r) \supset \mathcal {R} = \{n \in \mathbb {N} \colon \widetilde {\mu }(A_r \cap S^{-n}A_r)>0\}$ , so $\underline {d}(\overline {G}(F^r)) \geqslant \underline {d}(\mathcal {R})$ . By the mean ergodic theorem

$$ \begin{align*}\underline{d}(\mathcal{R}) = \liminf_{N \to \infty} \frac{1}{N+1} \sum_{n=0}^N 1_{\mathcal{R}}(n) \geqslant \liminf_{N \to \infty}\frac{1}{N+1} \sum_{n=0}^N \frac{\widetilde{\mu}(A_r \cap S^{-n}A_r)}{\widetilde{\mu}(A_r)} \geqslant \widetilde{\mu}(A_r),\end{align*} $$

and the theorem follows from inequality (2) of Theorem 3.4.

Using Theorem 3.5 in place of Theorem 3.4 in the above argument, we obtain the second inequality after taking $\epsilon \to 0$ .

Theorem 4.1 is also immediate from the corresponding result for $D^{r,2}$ , which we now prove.

Theorem 4.2. Let T be a tree. For $2 \leqslant r \leqslant q$ we have

$$ \begin{align*}\underline{d}(\overline{G}(D^{r,2})) \geqslant \frac{\overline{\dim}_MT - \log_q(r-1)}{1-\log_q(r-1)} \quad\mbox{and}\quad \underline{d}(G^*(D^{r,2})) \geqslant \frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)}.\end{align*} $$

Proof. We start with the proof of the first inequality. The idea is to show that $\overline {G}(D^{r,2})$ essentially contains the set of return times of $A_r$ , the density of which we can bound from below by the mean ergodic theorem. First observe that, by Proposition 2.10,

$$ \begin{align*} &\bigg| \int_{\widetilde{M}} \phi_{D_n^{r,2}}\,d\widetilde{\mu}-r!\int_{\widetilde{M}} \sum_{\substack{I \subset \Lambda \\ |I|=r}} \prod_{i \in I} \kern-1ptP(1_{B_i}P^{n-1}\overline{1_{A_r}} ) \,d \widetilde{\mu} \bigg| \\ &\quad=\bigg|r!\int_{\widetilde{M}}\kern-1pt \sum_{\substack{I \subset \Lambda \\ |I|=r}} \kern-1pt\prod_{i \in I} P(1_{B_i}P^{n-1}1_{A_r} ) \,d \widetilde{\mu} -r!\!\int_{\widetilde{M}}\kern-1pt \sum_{\substack{I \subset \Lambda \\ |I|=r}} \kern-1pt\prod_{i \in I} \kern-1pt P(1_{B_i}P^{n-1}\overline{1_{A_r}} ) \,d \widetilde{\mu} \bigg| \kern-1pt\xrightarrow{n\to\infty}\kern-1pt 0. \end{align*} $$

As $\overline {1_{A_r}} \in \mathscr {H}^{\infty }$ , by Lemma 2.9 $P^{n-1}\overline {1_{A_r}} = SP^n\overline {1_{A_r}}$ . Then, by Lemma 2.8 and orthogonality,

$$ \begin{align*}r!\int_{\widetilde{M}} \sum_{\substack{I \subset \Lambda \\ |I|=r}} \prod_{i \in I} P(1_{B_i}P^{n-1}\overline{1_{A_r}} ) \,d \widetilde{\mu} = \int_{\widetilde{M}} \overline{\varphi_{D^{r,1}_n}}(P^n\overline{1_{A_r}})^r\,d\widetilde{\mu}, \end{align*} $$

recalling $\varphi _{D^{r,1}_n} = r!\sum _{\substack {I \subset \Lambda \\ |I|=r}} \prod _{i \in I} P1_{B_i}$ . Define $Z_{\rho } = \{\widetilde {\tau } \in \widetilde {M} \colon \overline {\varphi _{D^{r,1}_n}}(\widetilde {\tau })\geqslant \rho \}$ , and observe it is well-defined up to a $\widetilde {\mu }$ -null set. As $0 \leqslant \varphi _{D^{r,1}_n} \leqslant 1_{A_r} \leqslant 1$ and $0 \leqslant \rho 1_{Z_{\rho }} \leqslant \overline {\varphi _{D^{r,1}_n}} \leqslant 1$ , the positivity of both P and conditional expectation imply

$$ \begin{align*}\int_{\widetilde{M}} \overline{\varphi_{D^{r,1}_n}}(P^n\overline{1_{A_r}})^r\,d\widetilde{\mu} \geqslant \int_{\widetilde{M}} \overline{\varphi_{D^{r,1}_n}}(P^n\overline{\varphi_{D^{r,1}_n}})^r\,d\widetilde{\mu} \geqslant \rho^{r+1}\int_{\widetilde{M}} 1_{Z_{\rho}} (P^n 1_{Z_{\rho}})^r\,d\widetilde{\mu}.\end{align*} $$

By Jensen’s inequality and the adjointness of P and S

$$ \begin{align*}\int_{\widetilde{M}} 1_{Z_{\rho}} (P^n 1_{Z_{\rho}})^r\,d\widetilde{\mu} \geqslant \bigg(\int_{\widetilde{M}} 1_{Z_{\rho}}P^n1_{Z_{\rho}} \,d\widetilde{\mu}\bigg)^r = \widetilde{\mu}(Z_{\rho} \cap S^{-n}Z_{\rho})^r.\end{align*} $$

Combining the above with the correspondence principle Theorem 3.4, it follows that $\overline {G}(D^{r,2})$ contains a cofinite subset of

$$ \begin{align*}\mathcal{R}^{\delta}(Z_{\rho}) = \{n \in \mathbb{N} \colon \widetilde{\mu}(Z_{\rho} \cap S^{-n}Z_{\rho})> \delta\widetilde{\mu}(Z_{\rho})^2\}\end{align*} $$

for all $\delta , \rho>0$ (because S is $\widetilde {\mu }$ -preserving). Therefore,

$$ \begin{align*} \underline{d}(\overline{G}(D^{r,2})) \geqslant \underline{d}(\mathcal{R}^{\delta}(Z_{\rho})) &\geqslant \liminf_{N \to \infty} \frac{1}{N+1} \sum_{\substack{n \leqslant N \\ n \in \mathcal{R}^{\delta}(Z_{\rho})}} \frac{\widetilde{\mu}(Z_{\rho} \cap S^{-n}Z_{\rho})}{\widetilde{\mu}(Z_{\rho})} \\ &\geqslant \bigg( \liminf_{N \to \infty} \frac{1}{N+1} \sum_{n=0}^N \frac{\widetilde{\mu}(Z_{\rho} \cap S^{-n}Z_{\rho})}{\widetilde{\mu}(Z_{\rho})} \bigg) - \delta\widetilde{\mu}(Z_{\rho}) \\ &\geqslant (1-\delta)\widetilde{\mu}(Z_{\rho}) \xrightarrow{\delta \to 0} \widetilde{\mu}(Z_{\rho}) \xrightarrow{\rho \to 0} \widetilde{\mu}(Z), \end{align*} $$

where the last inequality follows from the mean ergodic theorem and

$$ \begin{align*}Z = \{\widetilde{\tau} \in \widetilde{M} \colon \overline{\varphi_{D^{r,1}_n}}(\widetilde{\tau})>0\}.\end{align*} $$

As $1_{Z^c} \in L^{\infty }(\widetilde {M},\mathscr {B}_{\infty })$ , properties of the conditional expectation give

$$ \begin{align*}0=\int_{\widetilde{M}} 1_{Z^c} \overline{\varphi_{D^{r,1}_n}}\,d\widetilde{\mu} = \int_{\widetilde{M}} 1_{Z^c}\varphi_{D^{r,1}_n}\,d\widetilde{\mu}.\end{align*} $$

Hence, $Z \supset \{\widetilde {\tau } \in \widetilde {M} \colon \varphi _{D^{r,1}_n}(\widetilde {\tau })>0\} = A_r$ up to a $\widetilde {\mu }$ -null set, so $\underline {d}(\overline {G}(D^{r,2})) \geqslant \widetilde {\mu }(A_r)$ . The theorem then follows from inequality (2) of Theorem 3.4.

Using Theorem 3.5 in place of Theorem 3.4 in the above proofs, we obtain the second inequality after taking $\epsilon \to 0$ .

5 Inverse theorems for return times

Let $(X,\mathscr {B},\nu ,S)$ be a measure-preserving system and let A be a measurable set with $\nu (A)>0$ . If $\mathcal {R} = \{n \in \mathbb {N} \colon \nu (A \cap S^{-n}A)>0\}$ is the set of return times of A, then by the mean ergodic theorem $\underline {d}(\mathcal {R}) \geqslant \nu (A)$ .

Theorem 5.1. If $\overline {d}(\mathcal {R}) = \nu (A)> 0$ , then there exists an integer $m \geqslant 1$ such that up to $\nu $ -null sets

$$ \begin{align*}X = \bigsqcup_{i=0}^{m-1} S^{-i} A. \end{align*} $$

Proof. Define $\mathcal {R}_{\gamma } = \{n \in \mathbb {N} \colon \nu (A \cap S^{-n}A) \geqslant (1-\gamma )\nu (A) \}$ .

Lemma 5.2. If $n \in \mathcal {R}_{\gamma }$ and $n' \in \mathcal {R}_{\gamma '}$ , then $n + n' \in \mathcal {R}_{\gamma +\gamma '}$ .

Proof. If $B \subset A$ , then $\nu (A \cap S^{-n}B) \geqslant \nu (B)-\gamma \nu (A)$ . For $B = A \cap S^{-n'}A$ we have

$$ \begin{align*}\nu(A \cap S^{-(n+n')}A) &\geqslant \nu(A \cap S^{-n}(A \cap S^{-n'}A)) \geqslant \nu(A \cap S^{-n'}A)-\gamma\nu(A)\\ &\geqslant (1-\gamma-\gamma')\nu(A),\end{align*} $$

so $n+n'\in \mathcal {R}_{\gamma +\gamma '}$ .

Lemma 5.3. If $0 < \gamma < \tfrac 12$ , then $d(\mathcal {R}_{\gamma }+\mathcal {R}_{\gamma })=d(\mathcal {R}_{\gamma })=d(\mathcal {R})$ .

Proof. Let $(N_k)_{k\geqslant 1}$ be an increasing sequence such that

$$ \begin{align*}d_{(N_k)}(\mathcal{R}_{\gamma}) = \lim_{k \to \infty} \frac{|\mathcal{R}_{\gamma} \cap \{0,\ldots,N_k\}|}{N_k+1}\end{align*} $$

exists. By the mean ergodic theorem

$$ \begin{align*} \nu(A) &\leqslant \lim_{k \to \infty} \frac{1}{N_k+1} \sum_{n=0}^{N_k} \frac{\nu(A \cap S^{-n}A)}{\nu(A)} = \lim_{k \to \infty} \frac{1}{N_k+1} \sum_{n=0}^{N_k} 1_{\mathcal{R}}(n) \frac{\nu(A \cap S^{-n}A)}{\nu(A)} \\ &\leqslant \lim_{k \to \infty} \frac{1}{N_k+1} \bigg( \sum_{n \in \mathcal{R}_{\gamma}, n \leqslant N_k} 1_{\mathcal{R}}(n) + \sum_{n \notin \mathcal{R}_{\gamma},n\leqslant N_k} 1_{\mathcal{R}}(n)(1-\gamma)\bigg) \\ &= d_{(N_k)}(\mathcal{R}_{\gamma}) + (1-\gamma)(d(\mathcal{R})-d_{(N_k)}(\mathcal{R}_{\gamma})). \end{align*} $$

Rearranging and using the assumption $d(\mathcal {R})=\nu (A)$ , it follows that

$$ \begin{align*}\nu(A) \leqslant d_{(N_k)}(\mathcal{R}_{\gamma}) \leqslant d(\mathcal{R})=\nu(A).\end{align*} $$

Hence, $d_{(N_k)}(\mathcal {R}_{\gamma })=d(\mathcal {R})$ . By Lemma 5.2 $\mathcal {R}_{\gamma }+\mathcal {R}_{\gamma } \subset \mathcal {R}_{2\gamma } \subset \mathcal {R}$ , so

$$ \begin{align*}d(\mathcal{R})=d_{(N_k)}(\mathcal{R}_{\gamma}) = \underline{d}_{(N_k)}(\mathcal{R}_{\gamma}) \leqslant \underline{d}_{(N_k)}(\mathcal{R}_{\gamma}+\mathcal{R}_{\gamma}) \leqslant \overline{d}_{(N_k)}(\mathcal{R}_{\gamma}+\mathcal{R}_{\gamma}) \leqslant d(\mathcal{R}).\end{align*} $$

Hence, $d_{(N_k)}(\mathcal {R}_{\gamma }+\mathcal {R}_{\gamma })$ exists and equals $d(\mathcal {R})$ . As $(N_k)_{k \geqslant 1}$ was arbitrary, the conclusion follows.

For $0 < \gamma < \tfrac 12$ , Lemma 5.3 and Kneser’s theorem [Kne53] (see also [Bil97, Theorem 1.1]) therefore imply the existence of $m \geqslant 1$ and $K \subset \{0,\ldots ,m-1\}$ such that:

• • $\mathcal {R}_{\gamma } \subset K + m \mathbb {N}$ ;

• • $|K+K| = 2 |K| -1$ , where the operation on the left hand side is in $ \mathbb {Z} /m \mathbb {Z}$ ; and

• • $\mathcal {R}_{\gamma } + \mathcal {R}_{\gamma } \subset K+K +m\mathbb {N}$ with $ |(K+K +m\mathbb {N}) \setminus (\mathcal {R}_{\gamma } + \mathcal {R}_{\gamma })| < \infty $ .

It follows that $K = \{0\}$ , so $\mathcal {R}_{\gamma } \subset m\mathbb {N}$ and $d(\mathcal {R})=d(\mathcal {R}_{\gamma })=d(\mathcal {R}_{\gamma }+\mathcal {R}_{\gamma })=m^{-1}$ . Further, for all $n \in \mathcal {R}$ there exists $\gamma>0$ small enough such that $n+\mathcal {R}_{\gamma } \subset \mathcal {R}$ by Lemma 5.2. As, in addition, $\mathcal {R}_{\gamma } \subset \mathcal {R}$ and $d(\mathcal {R})=d(\mathcal {R}_{\gamma })$ , it follows that $n \in m\mathbb {N}$ . Then the m sets $S^{-i}A$ , $0\leqslant i \leqslant m-1$ are disjoint (up to $\nu $ -null sets) and each of measure $m^{-1}$ .

Theorem 5.4. If $(X,\mathscr {B},\nu ,S)$ is ergodic and

$$ \begin{align*}0< \underline{d}(\mathcal{R}) < \tfrac{3}{2} \nu(A),\end{align*} $$

then there exists an integer $m \geqslant 1$ such that $\mathcal {R} = m \mathbb {N}$ and $X = \bigsqcup _{i=0}^{m-1} S^{-i} ( \bigcup _{j=0}^{\infty } S^{-jm}A )$ up to $\nu $ -null sets.

Proof. By [BFS21, §1.5] all the theorems in [BFS21] hold for ergodic $\mathbb {N}$ -actions, so [BFS21, Theorem 1.3] gives the existence of $m \geqslant 1$ such that $\mathcal {R} = m \mathbb {N}$ . Therefore, the sets

$$ \begin{align*}\bigcup_{j=0}^{\infty} S^{-jm}A , S^{-1}\bigg(\bigcup_{j=0}^{\infty} S^{-jm}A\bigg) ,\ldots,S^{-(m-1)}\bigg(\bigcup_{j=0}^{\infty} S^{-jm}A \bigg)\end{align*} $$

are mutually disjoint up to $\nu $ -null sets, and ergodicity implies that they partition X.

6 Inverse theorems for trees

In this section, we prove inverse results for Theorems 4.1 and 4.2 (Theorems 6.1, 6.2 and 6.4) using the results of the previous section.

Theorem 6.1. If T is a tree and $2 \leqslant r \leqslant q$ with

$$ \begin{align*}\overline{d}(\overline{G}(F^r)) =\frac{\overline{\dim}_MT - \log_q(r-1)}{1-\log_q(r-1)}>0,\end{align*} $$

then $\overline {\dim }_M T = m^{-1}(1-\log _q(r-1))+\log _q(r-1)$ for some positive integer m. Moreover, $\overline {d}(V_1^{r,m,mk})>0$ for every $k \geqslant 1$ .

Proof. Let $\mathcal {R} = \{n \in \mathbb {N} \colon \widetilde {\mu }(A_r \cap S^{-n}A_r)>0\}$ . Combining the proof of Theorem 4.1 and the hypothesis, we obtain

$$ \begin{align*}\overline{d}(\overline{G}(F^r)) \geqslant \overline{d}(\mathcal{R}) \geqslant \underline{d}(\mathcal{R}) \geqslant \widetilde{\mu}(A_r) \geqslant \frac{\overline{\dim}_MT - \log_q(r-1)}{1-\log_q(r-1)}=\overline{d}(\overline{G}(F^r)).\end{align*} $$

Therefore, $\widetilde {\mu }(A_r) = \overline {d}(\overline {G}(F^r)) = \overline {d}(\mathcal {R})=\underline {d}(\mathcal {R})$ is positive; by Theorem 5.1 it equals $m^{-1}$ for some positive integer m, and $\widetilde {M} =\bigsqcup _{i=0}^{m-1} S^{-i} A_r$ up to $\widetilde {\mu }$ -null sets.

The above also shows that equality holds in Proposition 2.7 for $\widetilde {\mu }$ , whence $\int _{A_r} H\,d\widetilde {\mu } = \widetilde {\mu }(A_r)$ and $\int _{A_r^c}H\,d\widetilde {\mu } = (1-\widetilde {\mu }(A_r))\log _q(r-1)$ . The bounds on H then imply $\widetilde {\mu }$ -almost everywhere equalities

(9) $$ \begin{align} \prod_{i \in \Lambda} P1_{B_i}=c_11_{A_r}=c_11_{A_q}, \end{align} $$

(10) $$ \begin{align} 1_{A_{r-1}}\sum_{\substack{I \subset \Lambda \\ |I|=r-1}} \prod_{i \in I} P1_{B_i} = 1_{A_r^c}\sum_{\substack{I \subset \Lambda \\ |I|=r-1}} \prod_{i \in I} P1_{B_i} = c_21_{A_r^c}, \end{align} $$

where $c_1 = q^{-q}$ and $c_2 = (r-1)^{1-r}$ .

Recall from §3.1 the operators $R_{r-1,1}$ and $R_{q,1}$ on $L^{\infty }(\widetilde {M},\widetilde {\mu })$ , which, for simplicity, we denote by $R_1$ and $R_2$ :

$$ \begin{align*}R_1f = \sum_{\substack{I \subset \Lambda \\ |I|=r-1}} \prod_{i \in I} P(1_{B_i}f); \quad R_2f = \prod_{i \in \Lambda} P(1_{B_i}f).\end{align*} $$

Using the facts determined previously, we compute $\phi _{V_1^{r,m,mk}}' =(R_2R_1^{m-1})^k1_{A_q}$ . In the following, equalities are only up to $\widetilde {\mu }$ -null sets. We compute first the case $k=1$ . Note that $A_q = S^{-m}A_q$ and $1_{S^{-i}A_q} = S1_{S^{-i+1}A_q}$ for $i \geqslant 1$ . By Lemma 2.8

$$ \begin{align*} R_11_{A_q} = \sum_{\substack{I \subset \Lambda \\ |I|=r-1}} \prod_{i \in I} P(1_{B_i}1_{A_q}) &= \sum_{\substack{I \subset \Lambda \\ |I|=r-1}}\prod_{i \in I} P(1_{B_i}S1_{S^{-m+1}A_q}) \\ &= 1_{S^{-m+1}A_q}\sum_{\substack{I \subset \Lambda \\ |I|=r-1}} \prod_{i \in I} P1_{B_i} \\ &= c_21_{S^{-m+1}A_q}, \end{align*} $$

where the last equality follows from (10) and the fact $S^{-m+1}A_q =S^{-m+1}A_r \subset A_r^c$ . As $R_1$ is homogeneous of degree $r-1$ , repeating this calculation gives

$$ \begin{align*}R_1^{m-1}1_{A_q}=c_2^{\sum_{j=0}^{m-2}(r-1)^j}1_{S^{-1}A_q}\end{align*} $$

and, hence,

$$ \begin{align*} \phi_{V_1^{r,m,m}}' &= R_2\bigg(c_2^{\sum_{j=0}^{m-2}(r-1)^j}1_{S^{-1}A_q}\bigg) \\ &= c_2^{q\sum_{j=0}^{m-2}(r-1)^j}\prod_{i \in \Lambda}P(1_{B_i}S1_{A_q}) \\ &= c_2^{q\sum_{j=0}^{m-2}(r-1)^j}1_{A_q}\prod_{i \in \Lambda} P1_{B_i} \\ &= c_1c_2^{q\sum_{j=0}^{m-2}(r-1)^j}1_{A_q}. \end{align*} $$

Letting $d_1 = c_1c_2^{q\sum _{j=0}^{m-2}(r-1)^j}$ and defining inductively $d_k = d_{k-1}^{q(r-1)^{m-1}}d_1$ , it follows that $\phi _{V_1^{r,m,mk}}' = d_k1_{A_q} \ \widetilde {\mu }$ -almost everywhere. Then

$$ \begin{align*}\overline{d}(V_1^{r,m,mk}) \geqslant \int_{\widetilde{M}} \phi_{V_1^{r,m,mk}}\,d\widetilde{\mu} \geqslant \int_{\widetilde{M}} \phi_{V_1^{r,m,mk}}'\,d\widetilde{\mu} = d_k\widetilde{\mu}(A_q) = \frac{d_k}{m}>0\end{align*} $$

for all $k \geqslant 1$ by the correspondence principle Theorem 3.4.

Theorem 6.2. If T is a tree and $2 \leqslant r \leqslant q$ with

(11) $$ \begin{align} \underline{d}(G^{\ast}(F^r)) =\frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)}>0 \end{align} $$

or

(12) $$ \begin{align} \overline{d}(G^{\ast}(D^{r,2})) =\frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)}>0, \end{align} $$

then $\dim T = m^{-1}(1-\log _q(r-1))+\log _q(r-1)$ for some positive integer m. Moreover, $d^{\ast }(V_1^{r,m,mk})>0$ for every $k \geqslant 1$ .

Proof. Fix $\epsilon>0$ small enough, and let $\mathcal {R} = \{n \in \mathbb {N} \colon \widetilde {\eta _{\epsilon }}(A_r \cap S^{-n}A_r)>0\}$ . In the case of (11), from the proof of Theorem 4.1 we have

$$ \begin{align*} \frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)} = \underline{d}(G^{\ast}(F^r)) \geqslant \underline{d}(\mathcal{R}) \geqslant \widetilde{\eta_{\epsilon}}(A_r) \geqslant \frac{\dim T - \epsilon -\log_q(r-1)}{1-\log_q(r-1)}, \end{align*} $$

so $\underline {d}(\mathcal {R}) \leqslant \tfrac 32\widetilde {\eta _{\epsilon }}(A_r)$ for small enough $\epsilon $ . By Theorem 5.4 there is a positive integer m such that $\mathcal {R}=m\mathbb {N}$ and $\widetilde {M} =\bigsqcup _{i=0}^{m-1} S^{-i} ( \bigcup _{j=0}^{\infty } S^{-mj}A_r ) $ up to $\widetilde {\eta _{\epsilon }}$ -null sets.

In the case of (12), we invoke the proof of Theorem 4.2. Recall that there exist a measurable set Z such that $A_r \subset Z$ modulo $\widetilde {\eta _{\epsilon }}$ -null sets and an increasing chain of measurable sets $(Z_{\rho })_{\rho>0}$ with $\bigcup _{\rho> 0} Z_{\rho } = Z$ such that for every $\delta> 0$ we have

$$ \begin{align*} \frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)} &= \overline{d}(G^{\ast}(D^{r,2})) \geqslant \overline{d}(\mathcal{R}^{\delta}(Z_{\rho})) \\ &\geqslant \bigg( \liminf_{N \to \infty} \frac{1}{N+1} \sum_{n=0}^N \frac{\widetilde{\eta_{\epsilon}}(Z_{\rho} \cap S^{-n}Z_{\rho})}{\widetilde{\eta_{\epsilon}}(Z_{\rho})} \bigg) - \delta\widetilde{\eta_{\epsilon}}(Z_{\rho}) \\ &\geqslant (1-\delta)\widetilde{\eta_{\epsilon}}(Z_{\rho}) \xrightarrow{\delta \to 0} \widetilde{\eta_{\epsilon}}(Z_{\rho}) \xrightarrow{\rho \to 0} \widetilde{\eta_{\epsilon}}(Z) \\ &\geqslant \widetilde{\eta_{\epsilon}}(A_r) \geqslant \frac{\dim T - \epsilon-\log_q(r-1)}{1-\log_q(r-1)}, \end{align*} $$

where $\mathcal {R}^{\delta }(Z_{\rho }) = \{ n \in \mathbb {N} \colon \widetilde {\eta _{\epsilon }}(Z_{\rho } \cap S^{-n}Z_{\rho })> \delta \widetilde {\eta _{\epsilon }}(Z_{\rho })^2\}$ . Hence, for small enough $\epsilon $ and $\rho $ the assumptions of Theorem A.3 are satisfied, so there exists $m \geqslant 1$ such that $\mathcal {R}(Z_{\rho }) = \mathcal {R}^{\delta }(Z_{\rho }) = m \mathbb {N}$ , where $\mathcal {R}(Z_{\rho }) = \{ n \in \mathbb {N} \colon \widetilde {\eta _{\epsilon }}(Z_{\rho } \cap S^{-n}Z_{\rho })> 0 \}$ . As this is true for all $\rho> 0$ small enough and $\mathcal {R} \subset \bigcup _{\rho> 0} \mathcal {R}(Z_{\rho })$ , we conclude that for $\epsilon $ small enough there exists $m \geqslant 1$ such that $\mathcal {R} \subset m \mathbb {N}$ . This immediately implies that ${\widetilde {M} =\bigsqcup _{i=0}^{m-1} S^{-i} ( \bigcup _{j=0}^{\infty } S^{-mj}A_r ) }$ up to $\widetilde {\eta _{\epsilon }}$ -null sets.

In both cases, for small $\epsilon $ the above inequalities force

$$ \begin{align*}\frac{\dim T - \log_q(r-1)}{1-\log_q(r-1)} = m^{-1},\end{align*} $$

and, hence,

(13) $$ \begin{align} \widetilde{\eta_{\epsilon}}(A_r) \geqslant (1 - \epsilon') m^{-1} = (1- \epsilon') \widetilde{\eta_{\epsilon}}\bigg(\bigcup_{j=0}^{\infty} S^{-mj}A_r\bigg), \end{align} $$

where $\epsilon ' \to 0$ as $\epsilon \to 0$ . We also have

$$ \begin{align*}\dim T \geqslant \widetilde{\eta_{\epsilon}}(A_r)+(1-\widetilde{\eta_{\epsilon}}(A_r))\log_q(r-1) \geqslant H(\widetilde{\eta_{\epsilon}}) \geqslant \dim T-\epsilon\end{align*} $$

and, hence, the pair of inequalities

(14) $$ \begin{align} \phantom{\hspace{-27pt}}\int_{A_r} H\,d\widetilde{\eta_{\epsilon}}&\geqslant \widetilde{\eta_{\epsilon}}(A_r)-\epsilon,\qquad\quad\qquad \end{align} $$

(15) $$ \begin{align} \int_{A_r^c} H\,d\widetilde{\eta_{\epsilon}} &\geqslant (1-\widetilde{\eta_{\epsilon}}(A_r))\log_q(r-1)-\epsilon. \end{align} $$

We denote by $\mathbf {A}_{\mathbf {r}} = \bigcup _{j=0}^{\infty } S^{-mj}A_r$ . Then we have $\widetilde {M} = \bigsqcup _{i=0}^{m-1} S^{-i}\mathbf {A}_{\mathbf {r}}$ . Given $\widetilde {\tau } \in \widetilde {M}$ and $E \subset \widetilde {M}$ , observe that $S^{-i}(\widetilde {\tau }) \subset E$ if and only if $P^i1_E(\widetilde {\tau }) = 1$ . For $i \geqslant 0$ , define $E_i$ to be $\mathbf {A}_{\mathbf {r}}$ if m divides i and $\mathbf {A}_{\mathbf {r}}^{\mathbf {c}}$ otherwise. Then the m-periodicity of $\mathbf {A}_{\mathbf {r}}$ and $\mathbf {A}_{\mathbf {r}}^{\mathbf {c}} =\bigsqcup _{i=1}^{m-1} S^{-i} \mathbf {A}_{\mathbf {r}}$ under $S^{-1}$ gives $\widetilde {\eta _{\epsilon }}$ -almost everywhere equalities

$$ \begin{align*}P^i1_{E_i} = P^iS^i1_{S^{-i}E_i} = 1_{S^{-i}E_i},\end{align*} $$

so the set $\mathbf {A}_{\mathbf {r'}} = \bigcap _{i \geqslant 0} \{ \widetilde {\tau } \in \widetilde {M} \colon P^i1_{E_i}(\widetilde {\tau }) = 1\}$ is a $\widetilde {\eta _{\epsilon }}$ -conull subset of $\mathbf {A}_{\mathbf {r}}$ .

Define for $\delta>0$ the set

$$ \begin{align*}A_\delta = \bigcap_{i=0}^{mk} \{ \widetilde{\tau} \in \mathbf{A}_{\mathbf{r'}} \colon P^iH(\widetilde{\tau}) \geqslant c_i-\delta\}, \quad c_i = \begin{cases} 1, & m \mid i,\\ \log_{q}(r-1) & \text{otherwise}. \end{cases} \end{align*} $$

It follows from (13) and inequalities (14) and (15) that by choosing $\epsilon $ small enough we can guarantee that $\widetilde {\eta _{\epsilon }}(A_\delta )> 0$ . We show the existence of $\delta $ such that the configuration $V_1^{r,m,mk}$ appears at the root of $|\tau _0|$ for every $\widetilde {\tau } = (\tau _i)_{i \leqslant 0} \in A_\delta $ . First note that, by the construction of $\mathbf {A}_{\mathbf {r'}}$ , if $\widetilde {\tau } \in A_\delta $ and $v \in |\tau _0|$ with $0 \leqslant l(v) \leqslant mk$ , then $H(\widetilde {\tau }^v) \leqslant c_{l(v)}$ . Hence, for ${0 \leqslant i \leqslant mk}$

(16) $$ \begin{align} c_i-\delta \leqslant P^iH(\widetilde{\tau}) = \sum_{l(v) = i} \tau_0(v)H(\widetilde{\tau}^v) \leqslant c_i. \end{align} $$

If $H(\widetilde {\tau })> \log _q(q-1)$ , then $\widetilde {\tau } \in A_q$ , and if $H(\widetilde {\tau })> \log _q(r-2)$ , then $\widetilde {\tau } \in A_{r-1}$ . To prove the appearance of $V_1^{r,m,mk}$ at the root of $|\tau _0|$ it therefore suffices to give sufficiently large lower bounds for $H(\widetilde {\tau }^v)$ for $l(v) \leqslant mk$ .

Lemma 6.3. For every $\delta _1,\delta _2>0$ there exists $\delta>0$ such that for $1 \leqslant j \leqslant mk+1$ : (a) the set $\{\tau _0(v) \colon \widetilde {\tau } \in A_\delta , v \in |\tau _0|(j)\} \subset [0,1]$ is contained in an interval of length less than $\delta _1$ ; and (b) for all $\widetilde {\tau } \in A_\delta $ and $v \in |\tau _0|$ with $l(v) \leqslant j-1$ we have $H(\widetilde {\tau }^v) \geqslant c_{l(v)}-\delta _2$ .

Proof. We prove both statements simultaneously by induction on j. For $j=1$ we have $H(\widetilde {\tau }) \geqslant 1-\delta $ for all $\widetilde {\tau } \in A_\delta $ by (16), so any $\delta < \delta _2$ suffices. Further, observe that H is a continuous function attaining its maximum at $\widetilde {\tau }$ such that $p_{\widetilde {\tau }}(B_i) =q^{-1}$ for all $i \in \Lambda $ . Hence, given $\delta _1>0$ , the set $\{\tau _0(v) \colon \widetilde {\tau } \in A_\delta , v \in |\tau _0|(1)\}$ is contained in an interval of length $<\delta _1$ (containing $q^{-1}$ ) for $\delta $ small enough.

Assuming the lemma is true for $j \leqslant i < mk+1$ , we prove it for $j=i+1$ . We first consider statement (b). For $w \in |\tau _0|(i)$ with $\widetilde {\tau } \in A_\delta $ the inequality (16) gives

$$ \begin{align*}c_i - \delta \leqslant P^iH(\widetilde{\tau}) = \sum_{l(v)=i} \tau_0(v)H(\widetilde{\tau}^v) \leqslant \tau_0(w)H(\widetilde{\tau}^w) + (1-\tau_0(w)) c_i,\end{align*} $$

and rearranging gives

$$ \begin{align*}c_i - \frac{\delta}{\tau_0(w)} \leqslant H(\widetilde{\tau}^w). \end{align*} $$

By statement (a) of the induction hypothesis,

$$ \begin{align*}\sup_{\widetilde{\tau} \in A_\delta, w \in |\tau_0|(i)} \frac{\delta}{\tau_0(w)} \to 0\end{align*} $$

as $\delta \to 0$ , so by taking $\delta $ small enough, statement (b) is satisfied for $j=i+1$ . Combining statement (a) for $j=i$ and statement (b) for $j=i+1$ with the same argument as in the base case proves statement (a), noting that if m does not divide j, then we consider maxima of H on $A_r^c$ .

It follows that any $V_1^{r,m,mk}$ -detecting function is positive on $A_\delta $ . By the correspondence principle Theorem 3.5 we have for all $\epsilon>0$

$$ \begin{align*}d^{\ast}(V_1^{r,m,mk}) \geqslant \int_{\widetilde{M}} \phi_{V_1^{r,m,mk}}\,d\widetilde{\eta_{\epsilon}} \geqslant \int_{A_\delta} \phi_{V_1^{r,m,mk}}\,d\widetilde{\eta_{\epsilon}}>0,\end{align*} $$

because $\widetilde {\eta _{\epsilon }}(A_\delta )>0$ .

Theorem 6.4. Let $\beta \kern1.2pt{<}\kern1.2pt 3/2$ and assume that

$$ \begin{align*} 0 < \underline {d}(G^{\ast }(F^r)) < \beta \cdot \frac{({\dim T - \log _q(r - 1)})}{({1-\log _q(r-1)})}. \end{align*} $$

Then there exists an integer $m \geqslant 1$ such that $m \mathbb {N} \subset G^{\ast }(F^r)$ .

Proof. For small enough $\epsilon>0$ , by the correspondence principle Theorem 3.5 and the proof of Theorem 4.1

$$ \begin{align*} \beta\widetilde{\eta_{\epsilon}}(A_r) \geqslant \beta \frac{\dim T-\epsilon-\log_q(r-1)}{1-\log_q(r-1)}>\underline{d}(G^{\ast}(F^r)) \geqslant \underline{d}(\mathcal{R}), \end{align*} $$

where $\mathcal {R} = \{ n \in \mathbb {N} \colon \widetilde {\eta _{\epsilon }}(A_r \cap S^{-n} A_r)> 0 \}$ . Theorem 5.4 then implies that there exists $m \geqslant 1$ such that $m\mathbb {N} = \mathcal {R} \subset G^{\ast }(F^r)$ .

Question 6.5. It follows from the work of Furstenberg and Weiss in [FW03] that for every k there exists n such that $d^{\ast }(D^{2,k}_n)>0$ provided that $\dim T>0$ . On the other hand, under the assumptions of Theorem 6.4, there exists $m\geqslant 1$ such that $m \mathbb {N} \subset G^{\ast }(F)$ . In analogy to Proposition 1.5, is it true that the stronger claim $d^{\ast }(D^{2,k}_m)> 0$ holds true for every k satisfying $(1-\beta ^{-1})k < 1$ ?