diff --git a/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb b/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb
index e068271..272ff41 100644
--- a/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb
+++ b/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb
@@ -461,10 +461,10 @@
    "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",
     "\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",
     "# A list of linear light profile `Gaussians` will be input here, which will then be used to fit the data.\n",
     "\n",
@@ -563,9 +563,9 @@
    "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",
@@ -625,9 +625,9 @@
    "source": [
     "total_gaussians = 15\n",
     "\n",
-    "# The sigma values of the Gaussians will be fixed to values spanning 0.01\" to 1.0\".\n",
+    "# The sigma values of the Gaussians will be fixed to values spanning 0.0001\" to 1.0\".\n",
     "\n",
-    "log10_sigma_list = np.linspace(-2, np.log10(1.0), total_gaussians)\n",
+    "log10_sigma_list = np.linspace(-4, np.log10(1.0), total_gaussians)\n",
     "\n",
     "bulge_gaussian_list = []\n",
     "\n",
diff --git a/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py b/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py
index 25be59d..a62ccef 100644
--- a/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py
+++ b/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py
@@ -261,10 +261,10 @@
 """
 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)
 
 # A list of linear light profile `Gaussians` will be input here, which will then be used to fit the data.
 
@@ -330,9 +330,9 @@
 """
 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.
 
@@ -381,9 +381,9 @@
 """
 total_gaussians = 15
 
-# The sigma values of the Gaussians will be fixed to values spanning 0.01" to 1.0".
+# The sigma values of the Gaussians will be fixed to values spanning 0.0001" to 1.0".
 
-log10_sigma_list = np.linspace(-2, np.log10(1.0), total_gaussians)
+log10_sigma_list = np.linspace(-4, np.log10(1.0), total_gaussians)
 
 bulge_gaussian_list = []