diff --git a/_episodes/04-higgs-search.md b/_episodes/04-higgs-search.md
index 881a08b..2ff1c5a 100644
--- a/_episodes/04-higgs-search.md
+++ b/_episodes/04-higgs-search.md
@@ -177,7 +177,7 @@ ax.hist(branches["data_A"]["m4l"])
 {: .output}
 
 ![m4lep_histogram_1]({{ page.root }}/fig/m4lep_histogram_1.png)
-
+>
 > ## Exercise
 >
 > Make the histogram of the variable `m4l` for sample `mc_363490.llll`.
@@ -402,7 +402,7 @@ for k in range(0, 3):
 ~~~
 {: .output}
 
-And then make a plot, actually, let's make 2 plots, with matplotlib we can add sub-plots to the figure, then, we will be able to compare the MC distribution without and with weights.
+And then make a plot, actually, let's make 2 plots, with matplotlib we can add sub-plots to the figure, then we will be able to compare the MC distribution without and with weights.
 
 ```python
 var_name = "m4l"
@@ -410,6 +410,16 @@ units = " [GeV]"
 ranges = [[80, 170]]
 bins = 24
 ```
+> ##Python Unpacking & Physics Logic
+>
+> In the code `fig, (ax_1, ax_2) = plt.subplots(1, 2)`, we use 'tuple unpacking'.
+> This is a very common pattern in Python when a function returns multiple values.
+> Here, `subplots` returns a figure object and an array of axes. We "unpack" them
+> into two separate variables, `ax_1` and `ax_2`, so we can plot different
+> distributions side-by-side.
+{: .callout}
+
+
 
 ```python
 fig, (ax_1, ax_2) = plt.subplots(1, 2)
@@ -497,6 +507,10 @@ ax.legend(fontsize=18, frameon=False)
 ```
 
 ![m4lep_histogram_5]({{ page.root }}/fig/m4lep_histogram_5.png)
+
+>
+
+
 > ## Exercise
 >
 > Modify a bit the previous code to include the ticks and text, in the text and axis labels use latex to achieve the final plot.