diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index aba3ee8769f7f0584c9e383bf43804c506351595..1239d71c5dd984814e00069e9596e4b22e70ba46 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -466,13 +466,13 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
       poly: Whether to output a polynomial expantion of the low-resolution data.
     """
     def __init__(self,
-                 channels:List[str]=[f"channel_{j}_{k}"
-                                     for j, k in product(range(1, 5), ["A", "B", "C", "D"])],
+                 channels:Tuple[str]=tuple(f"channel_{j}_{k}"
+                                     for j, k in product(range(1, 5), ["A", "B", "C", "D"])),
                  tof_start: Optional[int]=None,
                  delta_tof: Optional[int]=300,
                  poly: bool=False
                  ):
-        self.channels = channels
+        self.channels = list(channels)
         self.tof_start = tof_start
         self.delta_tof = delta_tof
         self.poly = poly
@@ -874,8 +874,8 @@ class Model(TransformerMixin, BaseEstimator):
 
     """
     def __init__(self,
-                 channels:List[str]=[f"channel_{j}_{k}"
-                                     for j, k in product(range(1, 5), ["A", "B", "C", "D"])],
+                 channels:Tuple[str]=tuple(f"channel_{j}_{k}" for j, k in
+                                           product(range(1, 5), ["A", "B", "C", "D"])),
                  pca_threshold: float=0.90,
                  high_res_fwhm: float=0,
                  tof_start: Optional[int]=None,
@@ -902,7 +902,7 @@ class Model(TransformerMixin, BaseEstimator):
                                 ('unc', UncertaintyHolder()),
                                 ])
         self.ood = {ch: UncorrelatedDeviation(sigma=5)
-                    for ch in channels+['full']}
+                    for ch in channels + ('full',)}
         if model_type == "bnn":
             self.fit_model = BNNModel(n_epochs=n_bnn_epochs)
         elif model_type == "bnn_rvm":