diff --git a/blueprint/src/sections/chernoff.tex b/blueprint/src/sections/chernoff.tex
index 1aabe230..18f8bb6e 100644
--- a/blueprint/src/sections/chernoff.tex
+++ b/blueprint/src/sections/chernoff.tex
@@ -4,7 +4,7 @@ \section{Chernoff divergence}
   \label{def:Chernoff}
   \lean{ProbabilityTheory.chernoffDiv}
   \leanok
-  \uses{def:Renyi}
+  \uses{def:Renyi, lem:renyi_properties}
     The Chernoff divergence of order $\alpha > 0$ between two measures $\mu$ and $\nu$ on $\mathcal X$ is
   \begin{align*}
     C_\alpha(\mu, \nu) = \inf_{\xi \in \mathcal P(\mathcal X)}\max\{R_\alpha(\xi, \mu), R_\alpha(\xi, \nu)\} \: .
@@ -28,6 +28,7 @@ \section{Chernoff divergence}
 This is $R_1 = \KL$ (by definition).
 \end{proof}
 
+
 \begin{lemma}[Symmetry]
   \label{lem:chernoff_symm}
   %\lean{}
@@ -40,6 +41,7 @@ \section{Chernoff divergence}
 Immediate from the definition.
 \end{proof}
 
+
 \begin{lemma}[Monotonicity]
   \label{lem:chernoff_mono}
   %\lean{}
@@ -53,6 +55,7 @@ \section{Chernoff divergence}
 Consequence of Lemma~\ref{lem:renyi_monotone}.
 \end{proof}
 
+
 \begin{lemma}
   \label{lem:chernoff_eq_max_renyi}
   %\lean{}
diff --git a/blueprint/src/sections/hellinger.tex b/blueprint/src/sections/hellinger.tex
index ba33ed03..6195fab8 100644
--- a/blueprint/src/sections/hellinger.tex
+++ b/blueprint/src/sections/hellinger.tex
@@ -6,7 +6,7 @@ \section{Hellinger distance}
   \label{def:Hellinger}
   \lean{ProbabilityTheory.sqHellinger}
   \leanok
-  \uses{def:fDiv}
+  \uses{def:fDiv, lem:fDiv_properties}
   Let $\mu, \nu$ be two measures. The squared Hellinger distance between $\mu$ and $\nu$ is
   \begin{align*}
     \Hsq(\mu, \nu) = D_f(\mu, \nu) \quad \text{with } f: x \mapsto \frac{1}{2}\left( 1 - \sqrt{x} \right)^2 \: .
diff --git a/blueprint/src/sections/renyi_divergence.tex b/blueprint/src/sections/renyi_divergence.tex
index 925c5bc5..04e7cb68 100644
--- a/blueprint/src/sections/renyi_divergence.tex
+++ b/blueprint/src/sections/renyi_divergence.tex
@@ -158,6 +158,7 @@ \subsection{Properties inherited from f-divergences}
 The function $x \mapsto \frac{1}{\alpha - 1}\log (1 + (\alpha - 1)x)$ is non-decreasing and $D_f$ satisfies the DPI (Theorem~\ref{thm:fDiv_data_proc_2}), hence we get the DPI for $R_\alpha$.
 \end{proof}
 
+
 \begin{lemma}
   \label{lem:renyi_data_proc_event}
   %\lean{}
@@ -457,3 +458,39 @@ \subsection{Chain rule and tensorization}
 \begin{proof}%\leanok
 \uses{thm:renyi_prod}
 \end{proof}
+
+
+TODO: find a way to hide the following dummy lemma
+\begin{lemma}[Dummy lemma: Renyi properties]
+  \label{lem:renyi_properties}
+  %\lean{}
+  \leanok
+  \uses{def:Renyi, def:condRenyi, def:renyiMeasure}
+  Dummy node to summarize properties of the Rényi divergence.
+\end{lemma}
+
+\begin{proof}\leanok
+\uses{
+  lem:renyiDiv_zero,
+  lem:renyiDiv_eq_top_iff_mutuallySingular_of_lt_one,
+  lem:renyi_eq_log_integral,
+  lem:renyi_eq_log_integral',
+  lem:renyi_symm,
+  lem:renyi_cgf,
+  lem:renyi_cgf_2,
+  thm:renyi_data_proc,
+  lem:renyi_data_proc_event,
+  lem:renyiMeasure_ac,
+  lem:rnDeriv_renyiMeasure,
+  lem:kl_renyiMeasure_eq,
+  cor:renyi_eq_add_kl,
+  lem:renyi_eq_inf_add_kl,
+  lem:renyi_eq_inf_kl,
+  thm:renyi_chain_rule,
+  thm:renyi_compProd_bayesInv,
+  cor:renyi_prod_two,
+  thm:renyi_prod,
+  lem:renyi_prod_n,
+  thm:renyi_prod_countable
+}
+\end{proof}
\ No newline at end of file