diff --git a/lcls_tools/common/data/emittance.py b/lcls_tools/common/data/emittance.py
index d27996cf..ae675982 100644
--- a/lcls_tools/common/data/emittance.py
+++ b/lcls_tools/common/data/emittance.py
@@ -11,6 +11,7 @@ def compute_emit_bmag(
     beamsize_squared: np.ndarray,
     rmat: np.ndarray,
     twiss_design: np.ndarray = None,
+    weighted_fit: bool = False,
     maxiter: int = None,
 ):
     """
@@ -40,6 +41,10 @@ def compute_emit_bmag(
         to each measurement in the respective batch for the calculation of Bmag
         (useful for quad scans).
 
+    weighted_fit : bool, default = False
+        Boolean specifying whether or not to weight the fitting loss function by the inverse beam
+        size. If True, smaller beam sizes will be prioritized in the non-linear curve fitting.
+
     maxiter : int, optional
         Maximum number of iterations to perform in nonlinear fitting (minimization algorithm).
 
@@ -98,8 +103,14 @@ def loss_torch(params):
             params = torch.reshape(params, [*beamsize_squared.shape[:-2], 3])
             sig = torch.stack(beam_matrix_tuple(params), dim=-1).unsqueeze(-1)
             # sig should now be shape batchshape x 3 x 1 (column vectors)
+            if weighted_fit:
+                weights = 1.0 / beamsize_squared.sqrt()
+            else:
+                weights = 1.0
             total_abs_error = (
-                (torch.sqrt(amat @ sig) - torch.sqrt(beamsize_squared)).abs().nansum()
+                (weights * (torch.sqrt(amat @ sig) - torch.sqrt(beamsize_squared)))
+                .abs()
+                .nansum()
             )
             return total_abs_error
 
@@ -119,8 +130,12 @@ def loss(params):
             params = np.reshape(params, [*beamsize_squared.shape[:-2], 3])
             sig = np.expand_dims(np.stack(beam_matrix_tuple(params), axis=-1), axis=-1)
             # sig should now be shape batchshape x 3 x 1 (column vectors)
+            if weighted_fit:
+                weights = 1.0 / np.sqrt(beamsize_squared)
+            else:
+                weights = 1.0
             total_abs_error = np.nansum(
-                np.abs(np.sqrt(amat @ sig) - np.sqrt(beamsize_squared))
+                np.abs(weights * (np.sqrt(amat @ sig) - np.sqrt(beamsize_squared)))
             )
             return total_abs_error
 
diff --git a/tests/unit_tests/lcls_tools/common/data/test_emittance.py b/tests/unit_tests/lcls_tools/common/data/test_emittance.py
index 8e49db50..afd7c60a 100644
--- a/tests/unit_tests/lcls_tools/common/data/test_emittance.py
+++ b/tests/unit_tests/lcls_tools/common/data/test_emittance.py
@@ -54,6 +54,109 @@ def test_emittance_calc(self):
             result["bmag"], np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]), rtol=1e-2
         )
 
+    def test_emittance_calc_with_weighted_fit(self):
+        # set up test values
+        beamsize_squared = np.array(
+            [
+                [[0.00299705], [0.12034662], [0.03792509]],
+                [[0.00259244], [0.23703413], [0.09598636]],
+            ]
+        )
+
+        rmat = np.array(
+            [
+                [
+                    [[-0.24254679, 1.04600053], [-1.17140665, 0.92885986]],
+                    [[-1.16646303, 0.71788384], [-0.8039517, -0.36251133]],
+                    [[-0.55801204, -0.55330746], [0.61964408, -1.17765613]],
+                ],
+                [
+                    [[-0.24254679, 1.04600053], [-1.17140665, 0.92885986]],
+                    [[-1.16646303, 0.71788384], [-0.8039517, -0.36251133]],
+                    [[-0.55801204, -0.55330746], [0.61964408, -1.17765613]],
+                ],
+            ]
+        )
+
+        twiss_design = np.array(
+            [
+                [
+                    [0.29970473, -1.58494282],
+                    [12.03466156, -9.17146332],
+                    [3.79250871, 3.01307483],
+                ],
+                [
+                    [0.1296221, -0.66578778],
+                    [11.85170667, -9.88871364],
+                    [4.79931781, 2.87869131],
+                ],
+            ]
+        )
+
+        # compute emittance & bmag
+        result = compute_emit_bmag(
+            beamsize_squared=beamsize_squared,
+            rmat=rmat,
+            twiss_design=twiss_design,
+            weighted_fit=True,
+        )
+
+        # compare results with ground-truth
+        assert np.allclose(result["emittance"], np.array([[0.01], [0.02]]), rtol=1e-2)
+        assert np.allclose(
+            result["bmag"], np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]), rtol=1e-2
+        )
+
+    def test_emittance_calc_with_nans(self):
+        # set up test values
+        beamsize_squared = np.array(
+            [
+                [[0.00299705], [0.12034662], [0.03792509]],
+                [[0.00259244], [0.23703413], [0.09598636]],
+            ]
+        )
+
+        rmat = np.array(
+            [
+                [
+                    [[-0.24254679, 1.04600053], [-1.17140665, 0.92885986]],
+                    [[-1.16646303, 0.71788384], [-0.8039517, -0.36251133]],
+                    [[-0.55801204, -0.55330746], [0.61964408, -1.17765613]],
+                ],
+                [
+                    [[-0.24254679, 1.04600053], [-1.17140665, 0.92885986]],
+                    [[-1.16646303, 0.71788384], [-0.8039517, -0.36251133]],
+                    [[-0.55801204, -0.55330746], [0.61964408, -1.17765613]],
+                ],
+            ]
+        )
+
+        twiss_design = np.array(
+            [
+                [
+                    [0.29970473, -1.58494282],
+                    [12.03466156, -9.17146332],
+                    [3.79250871, 3.01307483],
+                ],
+                [
+                    [0.1296221, -0.66578778],
+                    [11.85170667, -9.88871364],
+                    [4.79931781, 2.87869131],
+                ],
+            ]
+        )
+
+        # compute emittance & bmag
+        result = compute_emit_bmag(
+            beamsize_squared=beamsize_squared, rmat=rmat, twiss_design=twiss_design
+        )
+
+        # compare results with ground-truth
+        assert np.allclose(result["emittance"], np.array([[0.01], [0.02]]), rtol=1e-2)
+        assert np.allclose(
+            result["bmag"], np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]), rtol=1e-2
+        )
+
     def test_emittance_calc_with_broadcast_twiss(self):
         # set up test values
         beamsize_squared = np.array(