diff --git a/sample-project/scripts/simcluster b/sample-project/scripts/simcluster
index 93460f2..667c943 100644
--- a/sample-project/scripts/simcluster
+++ b/sample-project/scripts/simcluster
@@ -23,6 +23,8 @@ def main(argv):
                         help='the x dimension (in pixels) of the image')
     parser.add_argument('-y', type=int, default=512,
                         help='the y dimension (in pixels) of the image')
+    parser.add_argument('-sigma', type=float, default=1.,
+                        help='the std of the gaussian noise')
 
     # Add an argument to handle the output file.  If we use argparse.FileType it will
     # handle opening a writeable file (and ensuring we can write to it).
@@ -33,7 +35,7 @@ def main(argv):
 
     args = parser.parse_args(argv)
 
-    image = simulated_cluster(n_stars=args.stars, dimensions=(args.x, args.y))
+    image = simulated_cluster(n_stars=args.stars, dimensions=(args.x, args.y), sigma=args.sigma)
 
     # For now the file writing is simple enough that we leave it in the main() function; in the future
     # we may want to break this out into its own routine that takes a filename and the image array
diff --git a/sample-project/simcluster/simcluster.py b/sample-project/simcluster/simcluster.py
index 6a66fd1..8ad593d 100644
--- a/sample-project/simcluster/simcluster.py
+++ b/sample-project/simcluster/simcluster.py
@@ -6,12 +6,14 @@
 
 CLUSTER_DEFAULTS = {
     'stars': 10000,
-    'dimensions': (512, 512)
+    'dimensions': (512, 512),
+    'sigma': 1.
 }
 
 
 def simulated_cluster(n_stars=CLUSTER_DEFAULTS['stars'],
-                      dimensions=CLUSTER_DEFAULTS['dimensions']):
+                      dimensions=CLUSTER_DEFAULTS['dimensions'],
+                      sigma=CLUSTER_DEFAULTS['sigma']):
     """
     Generates an image simulating a cluster of stars, including
     a Gaussian filter and background noise.
@@ -55,7 +57,7 @@ def simulated_cluster(n_stars=CLUSTER_DEFAULTS['stars'],
             image[y[idx], x[idx]] += fluxes[idx]
 
     # Convolve with a gaussian
-    image = gaussian_filter(image, sigma=1)
+    image = gaussian_filter(image, sigma=sigma)
 
     # Add noise
     image += np.random.normal(1., 0.001, image.shape)