diff -r a4137aedcb3a -r c3a4f4bb87f7 src/laser_sampling.py
--- a/src/laser_sampling.py	Thu Feb 26 09:32:12 2026 -0500
+++ b/src/laser_sampling.py	Wed Apr 22 22:32:00 2026 -0500
@@ -1,13 +1,12 @@
 import numpy as np
 from dolfinx import fem, geometry
+from dolfinx_access import cell_FunctionSpace_f64
 from mpi4py import MPI
 from petsc4py import PETSc
 from pointsource_pde.dolfinx_extras import get_mass_matrix
 from scipy.sparse.linalg import svds
 from slepc4py import SLEPc
 
-from dolfinx_access import cell_FunctionSpace_f64
-
 
 class LaserSampling:
     """
@@ -52,14 +51,14 @@
         E.setFromOptions()
         E.solve()
 
-        sigma = abs(E.getEigenvalue(0))
+        # sigma = abs(E.getEigenvalue(0))
         # self._opnorm = 1.0 / sigma
         # print("Full sampling opnorm = %f" % self._opnorm)
 
         # SVD operator norm
         _u, s, _v = svds(self.R, k=1)
         self._opnorm = s[0]  # / sigma
-        print(f"Laser sampling opnorm = {self._opnorm:.6f}")
+        # print(f"Laser sampling opnorm = {self._opnorm:.6f}")
 
     def matrix(self, beams):
         """Combined: generate beams + segment + cell collision + dof weighting"""
@@ -161,21 +160,17 @@
     def lipschitz_factor(self, *args):
         return self.opnorm(*args)
 
-    def diff_adj_lipschitz_factor(self, *args):
+    def diff_chain_lipschitz_factor(self, *args):
         return self.opnorm(*args)
 
     def diff_bound(self, *args):
         return self.opnorm(*args)
 
-    def codomain_bound(self, xbound=None):
-        if xbound is None:
-            raise Exception("Linear operators have unbounded range")
-        else:
-            return self.opnorm() * xbound
-
     # Construct observation noise
-    def noise(self, noise_level):
+    def noise(self, noise_level, rng=None):
+        if rng is None:
+            rng = np.random.default_rng()
         M = self.M
         # Generate noise with shape (M, 1) using scalar standard deviation
-        noise = np.random.normal(0, noise_level, size=(M,))
+        noise = rng.normal(0, noise_level, size=(M,))
         return noise