diff --git a/numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy b/numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy
index 776daf7a..147d00ad 100644
--- a/numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy
+++ b/numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy
@@ -6,25 +6,26 @@
 
 package inr.numass.scripts
 
-import hep.dataforge.maths.integration.GaussRuleIntegrator;
-import hep.dataforge.maths.integration.UnivariateIntegrator;
-import inr.numass.models.LossCalculator;
+import hep.dataforge.maths.integration.GaussRuleIntegrator
+import hep.dataforge.maths.integration.UnivariateIntegrator
+import inr.numass.models.LossCalculator
 import org.apache.commons.math3.analysis.UnivariateFunction
 
 UnivariateIntegrator integrator = new GaussRuleIntegrator(400);
-def exPos = 12.878;
-def ionPos = 13.86;
-def exW = 1.32;
-def ionW = 12.47;
-def exIonRatio = 3.96;
 
-def cutoff = 25d
+def exPos = 12.587;
+def ionPos = 11.11;
+def exW = 1.20;
+def ionW = 11.02;
+def exIonRatio = 2.43;
+
+def cutoff = 20d
 
 UnivariateFunction loss = LossCalculator.getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
 
 
 println integrator.integrate(loss,0,600);
-println integrator.integrate(loss,0,cutoff);
+println integrator.integrate(loss,0, cutoff);
 println integrator.integrate(loss,cutoff,600d);
 
 println (integrator.integrate(loss,0,cutoff) + integrator.integrate(loss,cutoff,3000d));
diff --git a/numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy b/numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy
index 14e54d92..54293e9e 100644
--- a/numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy
+++ b/numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy
@@ -8,39 +8,53 @@ package inr.numass.scripts
 
 import inr.numass.models.LossCalculator
 
-LossCalculator loss = new LossCalculator();
+LossCalculator loss = LossCalculator.instance()
 
-def X = 0.6
+def X = 0.34
 
 def lossProbs = loss.getGunLossProbabilities(X);
 
 printf("%8s\t%8s\t%8s\t%8s\t%n",
-    "eps",
-    "p1",
-    "p2",
-    "p3"
+        "eps",
+        "p1",
+        "p2",
+        "p3"
 )
 
-def singleScatter = loss.getSingleScatterFunction();
+/*
+'exPos'	= 12.587 ± 0.049
+'ionPos'	= 11.11 ± 0.50
+'exW'	= 1.20 ± 0.12
+'ionW'	= 11.02 ± 0.68
+'exIonRatio'	= 2.43 ± 0.42
+ */
 
-for(double d = 0; d < 30; d += 0.3){
+def singleScatter = loss.getSingleScatterFunction(
+        12.587,
+        11.11,
+        1.20,
+        11.02,
+        2.43
+);
+
+for (double d = 0; d < 30; d += 0.3) {
     double ei = 18500;
-    double ef = ei-d;
+    double ef = ei - d;
     printf("%8f\t%8f\t%8f\t%8f\t%n",
-        d,
-        lossProbs[1]*loss.getLossValue(1,ei,ef),
-        lossProbs[2]*loss.getLossValue(2,ei,ef),
-        lossProbs[3]*loss.getLossValue(3,ei,ef)
+            d,
+            lossProbs[1] * singleScatter.value(ei - ef),
+            lossProbs[2] * loss.getLossValue(2, ei, ef),
+            lossProbs[3] * loss.getLossValue(3, ei, ef)
     )
 }
 
-for(double d = 30; d < 100; d += 1){
+for (double d = 30; d < 100; d += 1) {
     double ei = 18500;
-    double ef = ei-d;
+    double ef = ei - d;
     printf("%8f\t%8f\t%8f\t%8f\t%n",
-        d,
-        lossProbs[1]*loss.getLossValue(1,ei,ef),
-        lossProbs[2]*loss.getLossValue(2,ei,ef),
-        lossProbs[3]*loss.getLossValue(3,ei,ef)
+            d,
+            lossProbs[1] * singleScatter.value(ei - ef),
+            lossProbs[2] * loss.getLossValue(2, ei, ef),
+            lossProbs[3] * loss.getLossValue(3, ei, ef)
     )
 }
diff --git a/numass-main/src/main/java/inr/numass/models/LossCalculator.java b/numass-main/src/main/java/inr/numass/models/LossCalculator.java
index de7e506c..91a72f44 100644
--- a/numass-main/src/main/java/inr/numass/models/LossCalculator.java
+++ b/numass-main/src/main/java/inr/numass/models/LossCalculator.java
@@ -178,12 +178,12 @@ public class LossCalculator {
             final LossCalculator loss = LossCalculator.instance;
             final List<Double> probs = loss.getGunLossProbabilities(set.getDouble("X"));
             UnivariateFunction single = (double e) -> probs.get(1) * scatterFunction.value(e);
-            frame.add(PlottableXYFunction.plotFunction("Single scattering", x -> single.value(x), 0, 100, 1000));
+            frame.add(PlottableXYFunction.plotFunction("Single scattering", single::value, 0, 100, 1000));
 
             for (int i = 2; i < probs.size(); i++) {
                 final int j = i;
                 UnivariateFunction scatter = (double e) -> probs.get(j) * loss.getLossValue(j, e, 0d);
-                frame.add(PlottableXYFunction.plotFunction(j + " scattering", x -> scatter.value(x), 0, 100, 1000));
+                frame.add(PlottableXYFunction.plotFunction(j + " scattering", scatter::value, 0, 100, 1000));
             }
 
             UnivariateFunction total = (eps) -> {
@@ -197,11 +197,11 @@ public class LossCalculator {
                 return sum;
             };
 
-            frame.add(PlottableXYFunction.plotFunction("Total loss", x -> total.value(x), 0, 100, 1000));
+            frame.add(PlottableXYFunction.plotFunction("Total loss", total::value, 0, 100, 1000));
 
         } else {
 
-            frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x -> scatterFunction.value(x), 0, 100, 2000));
+            frame.add(PlottableXYFunction.plotFunction("Differential crosssection", scatterFunction::value, 0, 100, 2000));
         }
 
     }
@@ -261,9 +261,7 @@ public class LossCalculator {
 
     public BivariateFunction getLossFunction(int order) {
         assert order > 0;
-        return (double Ei, double Ef) -> {
-            return getLossValue(order, Ei, Ef);
-        };
+        return (double Ei, double Ef) -> getLossValue(order, Ei, Ef);
     }
 
     public List<Double> getLossProbDerivs(double X) {