diff --git a/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb b/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb
index 2317fb1..4cf9795 100644
--- a/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb
+++ b/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb
@@ -452,9 +452,9 @@
    "source": [
     "total_gaussians = 30\n",
     "\n",
-    "# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0\".\n",
+    "# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0\".\n",
     "mask_radius = 3.0\n",
-    "log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)\n",
+    "log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)\n",
     "\n",
     "# By defining the centre here, it creates two free parameters that are assigned below to all Gaussians.\n",
     "\n",
@@ -560,9 +560,9 @@
    "source": [
     "total_gaussians = 30\n",
     "\n",
-    "# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0\".\n",
+    "# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0\".\n",
     "mask_radius = 3.0\n",
-    "log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)\n",
+    "log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)\n",
     "\n",
     "# By defining the centre here, it creates two free parameters that are assigned below to all Gaussians.\n",
     "\n",
@@ -621,9 +621,9 @@
    "source": [
     "total_gaussians = 10\n",
     "\n",
-    "# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0\".\n",
+    "# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0\".\n",
     "mask_radius = 3.0\n",
-    "log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)\n",
+    "log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)\n",
     "\n",
     "disk_gaussian_list = []\n",
     "\n",
diff --git a/scripts/chapter_2_modeling/tutorial_5_linear_profiles.py b/scripts/chapter_2_modeling/tutorial_5_linear_profiles.py
index 865394a..cac2d39 100644
--- a/scripts/chapter_2_modeling/tutorial_5_linear_profiles.py
+++ b/scripts/chapter_2_modeling/tutorial_5_linear_profiles.py
@@ -250,9 +250,9 @@
 """
 total_gaussians = 30
 
-# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0".
+# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0".
 mask_radius = 3.0
-log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)
+log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)
 
 # By defining the centre here, it creates two free parameters that are assigned below to all Gaussians.
 
@@ -325,9 +325,9 @@
 """
 total_gaussians = 30
 
-# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0".
+# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0".
 mask_radius = 3.0
-log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)
+log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)
 
 # By defining the centre here, it creates two free parameters that are assigned below to all Gaussians.
 
@@ -375,9 +375,9 @@
 """
 total_gaussians = 10
 
-# The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0".
+# The sigma values of the Gaussians will be fixed to values spanning a tenth of the pixel scale to the mask radius, 3.0".
 mask_radius = 3.0
-log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)
+log10_sigma_list = np.linspace(np.log10(dataset.pixel_scales[0] / 10.0), np.log10(mask_radius), total_gaussians)
 
 disk_gaussian_list = []