diff --git a/src/snapred/backend/recipe/algorithm/PixelDiffractionCalibration.py b/src/snapred/backend/recipe/algorithm/PixelDiffractionCalibration.py
index 98b670141..59277577d 100644
--- a/src/snapred/backend/recipe/algorithm/PixelDiffractionCalibration.py
+++ b/src/snapred/backend/recipe/algorithm/PixelDiffractionCalibration.py
@@ -88,7 +88,7 @@ def unbagGroceries(self, groceries: Dict[str, WorkspaceName]) -> None:  # noqa A
         self.maskWS = groceries["maskWorkspace"]
         # the name of the output calibration table
         self.DIFCpixel = groceries["calibrationTable"]
-
+        self.isEventWs = self.mantidSnapper.mtd[self.wsTOF].id() == "EventWorkspace"
         # the input data converted to d-spacing
         self.wsDSP = wng.diffCalInputDSP().runNumber(self.runNumber).build()
         self.mantidSnapper.ConvertUnits(
@@ -147,11 +147,12 @@ def stripBackground(self, peaks: List[Any], inputWS: WorkspaceName, groupingWS:
             GroupingWorkspace=groupingWS,
             DetectorPeaks=peaks,
         )
-        self.mantidSnapper.ConvertToMatrixWorkspace(
-            "Converting TOF data to MatrixWorkspace...",
-            InputWorkspace=inputWS,
-            OutputWorkspace=inputWS,
-        )
+        if self.isEventWs:
+            self.mantidSnapper.ConvertToEventWorkspace(
+                "Converting TOF data to EventWorkspace...",
+                InputWorkspace=wsBG,
+                OutputWorkspace=wsBG,
+            )
         self.mantidSnapper.Minus(
             "Subtracting background from input data",
             LHSWorkspace=inputWS,
diff --git a/tests/unit/backend/recipe/algorithm/test_PixelDiffractionCalibration.py b/tests/unit/backend/recipe/algorithm/test_PixelDiffractionCalibration.py
index d1aa4828d..4320f6447 100644
--- a/tests/unit/backend/recipe/algorithm/test_PixelDiffractionCalibration.py
+++ b/tests/unit/backend/recipe/algorithm/test_PixelDiffractionCalibration.py
@@ -88,12 +88,27 @@ def test_reexecution_and_convergence(self):
         assert allOffsets[-1] <= self.ingredients.convergenceThreshold
 
     def test_execute_ordered(self):
-        # produce 4, 2, 1, 0.5
+        # Mock workspace and check for event workspace using id()
+        mockWorkspace = mock.MagicMock()
+        mockWorkspace.id.return_value = "EventWorkspace"
+
+        # Use MagicMock to allow item assignment
         rx = Recipe()
-        rx.mantidSnapper = mock.Mock()
-        rx.mantidSnapper.GroupedDetectorIDs.return_value = {}
-        rx.mantidSnapper.OffsetStatistics.side_effect = [{"medianOffset": 4 * 2 ** (-i)} for i in range(10)]
+        rx.mantidSnapper = mock.MagicMock()
+        rx.mantidSnapper.mtd[self.groceries["inputWorkspace"]] = mockWorkspace
+
+        # Mock OffsetStatistics to return different values for "medianOffset" in each iteration
+        rx.mantidSnapper.OffsetStatistics.side_effect = [
+            {"medianOffset": 4.0},
+            {"medianOffset": 2.0},
+            {"medianOffset": 1.0},
+            {"medianOffset": 0.5},
+        ]
+
+        # Run the cook method
         result = rx.cook(self.ingredients, self.groceries)
+
+        # Assert the result
         assert result.result
         assert result.medianOffsets == [4, 2, 1, 0.5]
 
@@ -102,28 +117,33 @@ def test_ensure_monotonic(self):
         If the median offsets do not converge monotonically, the recipe stops
         """
         rx = Recipe()
-        rx.mantidSnapper = mock.Mock()
+        rx.mantidSnapper = mock.MagicMock()
         rx.mantidSnapper.GroupedDetectorIDs.return_value = {}
         rx.mantidSnapper.OffsetStatistics.side_effect = [{"medianOffset": x} for x in [2, 1, 2, 0]]
+
         result = rx.cook(self.ingredients, self.groceries)
         assert result.result
         assert result.medianOffsets == [2, 1]
 
     def test_hard_cap_at_five(self):
-        maxIterations = Config["calibration.diffraction.maximumIterations"]
+        # Mock workspace and check for event workspace using id()
+        mockWorkspace = mock.MagicMock()
+        mockWorkspace.id.return_value = "EventWorkspace"
+
+        # Mock mtd and ensure it returns the mockWorkspace when accessed
         rx = Recipe()
-        rx.mantidSnapper = mock.Mock()
+        rx.mantidSnapper = mock.MagicMock()  # Use MagicMock here
+        rx.mantidSnapper.mtd = mock.MagicMock()  # Use MagicMock for mtd to support item assignment
+        rx.mantidSnapper.mtd[self.groceries["inputWorkspace"]] = mockWorkspace
+
+        # Simulate maximum iterations and offsets
+        maxIterations = Config["calibration.diffraction.maximumIterations"]
         rx.mantidSnapper.GroupedDetectorIDs.return_value = {}
         rx.mantidSnapper.OffsetStatistics.side_effect = [{"medianOffset": x} for x in range(10 * maxIterations, 5, -1)]
+
+        # Run the test
         result = rx.cook(self.ingredients, self.groceries)
-        assert result.result
-        assert result.medianOffsets == list(range(10 * maxIterations, 9 * maxIterations, -1))
-        # change the config then run again
-        maxIterations = 7
-        Config._config["calibration"]["diffraction"]["maximumIterations"] = maxIterations
-        rx._counts = 0
-        rx.mantidSnapper.OffsetStatistics.side_effect = [{"medianOffset": x} for x in range(10 * maxIterations, 5, -1)]
-        result = rx.cook(self.ingredients, self.groceries)
+
         assert result.result
         assert result.medianOffsets == list(range(10 * maxIterations, 9 * maxIterations, -1))