diff --git a/sunkit_dem/base_model.py b/sunkit_dem/base_model.py
index 2e84b15..c2c7c37 100644
--- a/sunkit_dem/base_model.py
+++ b/sunkit_dem/base_model.py
@@ -58,9 +58,12 @@ def __init_subclass__(cls, **kwargs):
             cls._registry[cls] = cls.defines_model_for
 
     @u.quantity_input
-    def __init__(self, data, kernel, temperature_bin_edges: u.K, **kwargs):
+    def __init__(self, data, kernel, temperature_bin_edges: u.K, kernel_temperatures=None, **kwargs):
         self.temperature_bin_edges = temperature_bin_edges
         self.data = data
+        self.kernel_temperatures = kernel_temperatures
+        if self.kernel_temperatures is None:
+            self.kernel_temperatures = self.temperature_bin_centers
         self.kernel = kernel
 
     @property
@@ -72,9 +75,7 @@ def _keys(self):
     @property
     @u.quantity_input
     def temperature_bin_centers(self) -> u.K:
-        log_temperature = np.log10(self.temperature_bin_edges.value)
-        log_temperature_centers = (log_temperature[1:] + log_temperature[:-1])/2.
-        return u.Quantity(10.**log_temperature_centers, self.temperature_bin_edges.unit)
+        return (self.temperature_bin_edges[1:] + self.temperature_bin_edges[:-1])/2.
 
     @property
     def data(self) -> ndcube.NDCollection:
@@ -89,9 +90,23 @@ def data(self, data):
         if not isinstance(data, ndcube.NDCollection):
             raise ValueError('Input data must be an NDCollection')
         if not all([hasattr(data[k], 'unit') for k in data]):
-            raise u.UnitsError('Each NDCube in NDCubeSequence must have units')
+            raise u.UnitsError('Each NDCube in NDCollection must have units')
         self._data = data
 
+    @property
+    def combined_mask(self):
+        """
+        Combined mask of all members of ``data``. Will be True if any member is masked.
+        This is propagated to the final DEM result
+        """
+        combined_mask = []
+        for k in self._keys:
+            if self.data[k].mask is not None:
+                combined_mask.append(self.data[k].mask)
+            else:
+                combined_mask.append(np.full(self.data[k].shape, False))
+        return np.any(combined_mask, axis=0)
+        
     @property
     def kernel(self):
         return self._kernel
@@ -100,20 +115,20 @@ def kernel(self):
     def kernel(self, kernel):
         if len(kernel) != len(self.data):
             raise ValueError('Number of kernels must be equal to length of wavelength dimension.')
-        if not all([v.shape == self.temperature_bin_centers.shape for _,v in kernel.items()]):
+        if not all([v.shape == self.kernel_temperatures.shape for _, v in kernel.items()]):
             raise ValueError('Temperature bin centers and kernels must have the same shape.')
         self._kernel = kernel
 
     @property
     def data_matrix(self):
-        return np.stack([self.data[k].data*self.data[k].unit for k in self._keys])
+        return np.stack([self.data[k].data for k in self._keys])
 
     @property
     def kernel_matrix(self):
-        return np.stack([self.kernel[k] for k in self._keys])
+        return np.stack([self.kernel[k].value for k in self._keys])
 
     def fit(self, *args, **kwargs):
-        """
+        r"""
         Apply inversion procedure to data.
 
         Returns
@@ -126,9 +141,13 @@ def fit(self, *args, **kwargs):
         dem_dict = self._model(*args, **kwargs)
         wcs = self._make_dem_wcs()
         meta = self._make_dem_meta()
-        dem = ndcube.NDCube(dem_dict.pop('dem'),
+        dem_data = dem_dict.pop('dem')
+        mask = np.full(dem_data.shape, False)
+        mask[:,...] = self.combined_mask
+        dem = ndcube.NDCube(dem_data,
                             wcs,
                             meta=meta,
+                            mask=mask,
                             uncertainty=StdDevUncertainty(dem_dict.pop('uncertainty')))
         cubes = [('dem', dem),]
         for k in dem_dict: