diff --git a/doc/versionHistory.rst b/doc/versionHistory.rst
index eb1be469e..bfc164420 100644
--- a/doc/versionHistory.rst
+++ b/doc/versionHistory.rst
@@ -6,6 +6,14 @@
 Version History
 ##################
 
+.. _lsst.ts.wep-1.5.2:
+
+-------------
+1.5.2
+-------------
+
+* Fix the ``ZernikeMaskedFit()`` when passing masked data 
+
 .. _lsst.ts.wep-1.5.1:
 
 -------------
diff --git a/python/lsst/ts/wep/cwfs/DonutTemplateModel.py b/python/lsst/ts/wep/cwfs/DonutTemplateModel.py
index 47e30e431..ea7ea066d 100644
--- a/python/lsst/ts/wep/cwfs/DonutTemplateModel.py
+++ b/python/lsst/ts/wep/cwfs/DonutTemplateModel.py
@@ -101,7 +101,7 @@ def makeTemplate(
         sensorXPixel = float(sensorXMicron) / pixelSizeInUm
         sensorYPixel = float(sensorYMicron) / pixelSizeInUm
 
-        # Multiply by pixelScale then divide by 3600 for arcsec -> deg conversion
+        # Multiply by pixelScale then divide by 3600 for arcsec->deg conversion
         sensorXDeg = sensorXPixel * pixelScale / 3600
         sensorYDeg = sensorYPixel * pixelScale / 3600
         fieldXY = [sensorXDeg, sensorYDeg]
diff --git a/python/lsst/ts/wep/cwfs/Tool.py b/python/lsst/ts/wep/cwfs/Tool.py
index 9ea61e8b0..ebe812ec5 100644
--- a/python/lsst/ts/wep/cwfs/Tool.py
+++ b/python/lsst/ts/wep/cwfs/Tool.py
@@ -210,7 +210,7 @@ def ZernikeAnnularFit(s, x, y, numTerms, e, nMax=28):
         h[:, ii] = ZernikeAnnularEval(z, xFinite, yFinite, e, nMax=nMax)
 
     # Solve the equation: H*Z = S => Z = H^(-1)S
-    z = np.linalg.lstsq(h, s, rcond=None)[0]
+    z = np.linalg.lstsq(h, sFinite, rcond=None)[0]
 
     return z
 
diff --git a/tests/cwfs/test_tool.py b/tests/cwfs/test_tool.py
index d3fb19405..0b9577b52 100644
--- a/tests/cwfs/test_tool.py
+++ b/tests/cwfs/test_tool.py
@@ -30,6 +30,7 @@
     ZernikeAnnularGrad,
     ZernikeAnnularJacobian,
     ZernikeAnnularFit,
+    ZernikeMaskedFit,
     padArray,
     extractArray,
 )
@@ -219,6 +220,26 @@ def testZernikeAnnularFit(self):
         allOpdAns = np.loadtxt(ansOpdFilePath)
         self.assertLess(np.sum(np.abs(coef - allOpdAns[0, :])), 1e-10)
 
+    def testZernikeMaskFit(self):
+        e = 0.2
+        nc = 6
+        surface = ZernikeAnnularEval(self.zerCoef[0:nc], self.xx, self.yy, e)
+
+        # mask data
+        cut = -0.9
+        r = np.sqrt(self.xx**2 + self.yy**2)
+        idx = (r > 1) | (r < e) | (self.xx < cut)
+
+        xx = self.xx[:].copy()
+        yy = self.yy[:].copy()
+        xx[idx] = np.nan
+        yy[idx] = np.nan
+        mask = ~np.isnan(xx)
+
+        zr = ZernikeMaskedFit(surface, xx, yy, nc, mask, e)
+
+        self.assertLess(np.sum(np.abs(zr - self.zerCoef[0:nc])**2), 1e-10)
+
     def testPadArray(self):
 
         imgDim = 10