diff --git a/machine_learning_hep/analysis/analyzer_jets.py b/machine_learning_hep/analysis/analyzer_jets.py
index af3857a81e..8e844875b3 100644
--- a/machine_learning_hep/analysis/analyzer_jets.py
+++ b/machine_learning_hep/analysis/analyzer_jets.py
@@ -531,13 +531,14 @@ def fit(self):
                                 self.roo_ws_ptjet[level][jptjet][ipt] = roo_ws.Clone()
                             # TODO: take parameter names from DB
                             if level in ('data', 'mc'):
-                                varname_mean = fitcfg.get('var_mean', param_names["gauss_mean"])
-                                varname_sigma = fitcfg.get('var_sigma', param_names["gauss_sigma"])
+                                varname_mean = fitcfg.get('var_mean', self.p_param_names["gauss_mean"])
+                                varname_sigma = fitcfg.get('var_sigma', self.p_param_names["gauss_sigma"])
                                 self.fit_mean[level][ipt] = roo_ws.var(varname_mean).getValV()
                                 self.fit_sigma[level][ipt] = roo_ws.var(varname_sigma).getValV()
-                            varname_m = fitcfg.get('var', param_names["mass"])
-                            if roo_ws.pdf(self.p_pdfnames["pdf_bkg"]):
-                                self.fit_func_bkg[level][ipt] = roo_ws.pdf(self.p_pdfnames["pdf_bkg"]).asTF(roo_ws.var(varname_m))
+                            varname_m = fitcfg.get('var', self.p_param_names["mass"])
+                            pdf_bkg = roo_ws.pdf(self.p_pdfnames["pdf_bkg"])
+                            if pdf_bkg:
+                                self.fit_func_bkg[level][ipt] = pdf_bkg.asTF(roo_ws.var(varname_m))
                             self.fit_range[level][ipt] = (roo_ws.var(varname_m).getMin('fit'),
                                                           roo_ws.var(varname_m).getMax('fit'))
                             self.logger.debug('fit range for %s-%i: %s', level, ipt, self.fit_range[level][ipt])
diff --git a/machine_learning_hep/analysis/analyzerdhadrons.py b/machine_learning_hep/analysis/analyzerdhadrons.py
index 5f9296b5c2..31fcd5a720 100644
--- a/machine_learning_hep/analysis/analyzerdhadrons.py
+++ b/machine_learning_hep/analysis/analyzerdhadrons.py
@@ -313,8 +313,9 @@ def fit(self):
                                 self.fit_mean[level][ipt] = roo_ws.var('mean').getValV()
                                 self.fit_sigma[level][ipt] = roo_ws.var('sigma_g1').getValV()
                             var_m = fitcfg.get('var', 'm')
-                            if roo_ws.pdf(self.p_pdfnames["pdf_bkg"]):
-                                self.fit_func_bkg[level][ipt] = roo_ws.pdf(self.p_pdfnames["pdf_bkg"]).asTF(roo_ws.var(var_m))
+                            pdf_bkg = roo_ws.pdf(self.p_pdfnames["pdf_bkg"])
+                            if pdf_bkg:
+                                self.fit_func_bkg[level][ipt] = pdf_bkg.asTF(roo_ws.var(var_m))
                             self.fit_range[level][ipt] = (roo_ws.var(var_m).getMin(), roo_ws.var(var_m).getMax())
                         else:
                             self.logger.error('RooFit failed for %s bin %d', level, ipt)