diff --git a/tests/regression_tests/filter_energyfun/results_true.dat b/tests/regression_tests/filter_energyfun/results_true.dat
index a75b3fd4f7..4fedf2a0d7 100644
--- a/tests/regression_tests/filter_energyfun/results_true.dat
+++ b/tests/regression_tests/filter_energyfun/results_true.dat
@@ -1,2 +1,4 @@
    energyfunction nuclide                    score     mean  std. dev.
 0  448ee8dfd19c4f   Am241  ((n,gamma) / (n,gamma)) 1.74e-01   6.83e-03
+   energyfunction nuclide      score     mean  std. dev.
+0  37e006ae6b2e74   Am241  (n,gamma) 8.16e-02   2.24e-03
diff --git a/tests/regression_tests/filter_energyfun/test.py b/tests/regression_tests/filter_energyfun/test.py
index 9e8189f216..93eed33279 100644
--- a/tests/regression_tests/filter_energyfun/test.py
+++ b/tests/regression_tests/filter_energyfun/test.py
@@ -60,11 +60,17 @@ class FilterEnergyFunHarness(PyAPITestHarness):
         # Read the statepoint file.
         sp = openmc.StatePoint(self._sp_name)
 
+        dataframes_string = ""
         # Use tally arithmetic to compute the branching ratio.
         br_tally = sp.tallies[2] / sp.tallies[1]
+        dataframes_string += br_tally.get_pandas_dataframe().to_string() + '\n'
+
+        # Write out the log-log interpolation as well
+        log_tally = sp.tallies[3]
+        dataframes_string += log_tally.get_pandas_dataframe().to_string() + '\n'
 
         # Output the tally in a Pandas DataFrame.
-        return br_tally.get_pandas_dataframe().to_string() + '\n'
+        return dataframes_string
 
     def _compare_results(self):
         super()._compare_results()
@@ -96,7 +102,6 @@ class FilterEnergyFunHarness(PyAPITestHarness):
         assert all(sp_log_log_filt.energy == model_log_log_filt.energy)
         assert all(sp_log_log_filt.y == model_log_log_filt.y)
 
-
         # because the values of y are monotonically increasing,
         # we expect the log-log tally to have a higher value
         assert all(sp_lin_lin_tally.mean < sp_log_log_tally.mean)