Indeed, in that case the liminf of the quantities in the left-hand side of (6.2) are greater than or equal to the right-hand side, for any choice of the forest $\mathcal{F}_k$. As the sum of the quantities on the right-hand side of (6.2) over these choices is equal to 1, necessarily the desired convergence holds.
Let us thus show (6.5). Set $\varphi(l) := 4^{-l}C(l)$. We have
A s = ∑ l = 2 k + 1 ∞ φ ( l ) φ ( p ) ∑ q = 0 p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s − r = p − q ) . A_s = \sum_{l=2k+1}^{\infty} \frac{\varphi(l)}{\varphi(p)} \sum_{q=0}^{p} P_{l-(2k+1)}(Y_s=q) P_{m_k}(Y_{s-r}=p-q).A s = l = 2 k + 1 ∑ ∞ φ ( p ) φ ( l ) q = 0 ∑ p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s − r = p − q ) .
First, as $\theta$ is a critical offspring distribution, we get from [26] that, for any $q \ge 0$,
P m k ( Y s − r = p − q ) P m k ( Y s = p − q ) → s → ∞ 1. \frac{P_{m_k}(Y_{s-r} = p-q)}{P_{m_k}(Y_s = p-q)} \xrightarrow{s \to \infty} 1.P m k ( Y s = p − q ) P m k ( Y s − r = p − q ) s → ∞ 1.
Thus, for any $l \ge 2k+1$, for every $\varepsilon > 0$, for any sufficiently large $s$,
∑ q = 0 p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s − r = p − q ) ≥ ( 1 − ε ) ∑ q = 0 p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s = p − q ) ≥ ( 1 − ε ) P l − ( 2 k + 1 ) + m k ( Y s = p ) .
\begin{align*}
\sum_{q=0}^{p} P_{l-(2k+1)}(Y_s=q) P_{m_k}(Y_{s-r}=p-q) &\ge (1-\varepsilon) \sum_{q=0}^{p} P_{l-(2k+1)}(Y_s=q) P_{m_k}(Y_s=p-q) \\
&\ge (1-\varepsilon) P_{l-(2k+1)+m_k}(Y_s=p).
\end{align*}
q = 0 ∑ p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s − r = p − q ) ≥ ( 1 − ε ) q = 0 ∑ p P l − ( 2 k + 1 ) ( Y s = q ) P m k ( Y s = p − q ) ≥ ( 1 − ε ) P l − ( 2 k + 1 ) + m k ( Y s = p ) .
This implies that:
A s ≥ ( 1 − ε ) ∑ l = 2 k + 1 ∞ φ ( l ) φ ( p ) P l − ( 2 k + 1 ) + m k ( Y s = p ) . A_s \ge (1 - \varepsilon) \sum_{l=2k+1}^{\infty} \frac{\varphi(l)}{\varphi(p)} P_{l-(2k+1)+m_k} (Y_s = p).A s ≥ ( 1 − ε ) l = 2 k + 1 ∑ ∞ φ ( p ) φ ( l ) P l − ( 2 k + 1 ) + m k ( Y s = p ) .
Now, from the asymptotics of $C(l)$, we have that, for some $l_0 \ge 0$, for any $l \ge l_0$, we have
φ ( l ) ≥ ( 1 − ε ) φ ( l − ( 2 k + 1 ) + m k ) , \varphi(l) \ge (1 - \varepsilon)\varphi(l - (2k + 1) + m_k),φ ( l ) ≥ ( 1 − ε ) φ ( l − ( 2 k + 1 ) + m k ) ,
so that,
A s ≥ ( 1 − ε ) 2 ∑ l = m k ∨ l 0 ∞ φ ( l ) φ ( p ) P l ( Y s = p ) . A_s \ge (1 - \varepsilon)^2 \sum_{l=m_k \lor l_0}^{\infty} \frac{\varphi(l)}{\varphi(p)} P_l(Y_s = p).A s ≥ ( 1 − ε ) 2 l = m k ∨ l 0 ∑ ∞ φ ( p ) φ ( l ) P l ( Y s = p ) .
Recall that $\varphi(l) = lh(l)$, which yields:
A s ≥ ( 1 − ϵ ) 2 ∑ l = m k ∨ l 0 ∨ p ∞ h ( l ) h ( p ) P l ( Y s = p ) = ( 1 − ϵ ) 2 ( 1 − ∑ l = 0 m k ∨ l 0 ∨ p − 1 h ( l ) h ( p ) P l ( Y s = p ) ) ,
\begin{align*}
A_s &\ge (1-\epsilon)^2 \sum_{l=m_k \lor l_0 \lor p}^{\infty} \frac{h(l)}{h(p)} P_l(Y_s=p) \\
&= (1-\epsilon)^2 \left( 1 - \sum_{l=0}^{m_k \lor l_0 \lor p-1} \frac{h(l)}{h(p)} P_l(Y_s=p) \right),
\end{align*}
A s ≥ ( 1 − ϵ ) 2 l = m k ∨ l 0 ∨ p ∑ ∞ h ( p ) h ( l ) P l ( Y s = p ) = ( 1 − ϵ ) 2 ( 1 − l = 0 ∑ m k ∨ l 0 ∨ p − 1 h ( p ) h ( l ) P l ( Y s = p ) ) ,
the last equality stemming from (5.12).
Finally, we use once again the fact that, for any fixed $l$,
P l ( Y s = p ) → s → ∞ 0 , P_l (Y_s = p) \xrightarrow{s \to \infty} 0,P l ( Y s = p ) s → ∞ 0 ,
to get that, for any $\epsilon > 0$,
lim inf s A s ≥ ( 1 − ϵ ) 2 . \liminf_s A_s \ge (1 - \epsilon)^2.s lim inf A s ≥ ( 1 − ϵ ) 2 .
As $\epsilon$ was completely arbitrary in the above chain of arguments, we get that
lim inf s A s ≥ 1. \liminf_s A_s \ge 1.s lim inf A s ≥ 1.
This completes the proof of the proposition. $\square$