diff --git a/skimage/transform/_radon_transform.pyx b/skimage/transform/_radon_transform.pyx
index 65cf1023..321d9556 100644
--- a/skimage/transform/_radon_transform.pyx
+++ b/skimage/transform/_radon_transform.pyx
@@ -120,7 +120,7 @@ cpdef bilinear_ray_update(cnp.ndarray[cnp.double_t, ndim=2] image,
     cdef double s0
     s0 = sqrt(radius*radius - t*t) if radius**2 >= t**2 else 0.
     cdef unsigned int Ns = 2 * int(ceil(2 * s0))
-    cdef double hamming_beta = 0.46164
+    cdef double hamming_beta = 0.46164    # beta for equiripple Hamming window
 
     cdef double ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j
     cdef double hamming_window
diff --git a/skimage/transform/radon_transform.py b/skimage/transform/radon_transform.py
index ebf06d35..bba73c3f 100644
--- a/skimage/transform/radon_transform.py
+++ b/skimage/transform/radon_transform.py
@@ -257,13 +257,14 @@ def iradon(radon_image, theta=None, output_size=None,
     return reconstructed * np.pi / (2 * len(th))
 
 
-def _sart_order_angles(theta, tau=3.):
+def _sart_order_angles(theta):
     """
     Order angles to reduce the amount of correlated information
     in subsequent projections, i.e. make sure subsequent angles
     are as far away from each other mod 180 degrees as possible.
     Indices into the ``theta`` array are yielded.
     """
+    tau = 3.    # time constant for correlations; 0.1 < tau < 100 works well
     used_indices = [0]
     remaining_indices = range(1, len(theta))
     yield 0