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darksnake 2016-07-26 16:17:26 +03:00
parent 5081ce0b2c
commit fbcc8eef94
4 changed files with 76 additions and 14 deletions
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4
numass-main/src/main/groovy/inr/numass/scripts/SterileDemo.groovy
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@ -50,9 +50,9 @@ import hep.dataforge.io.FittingIOUtils
setDefault(Locale.US);
//ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), 8.3e-5, 13990d, 18600d);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), 8.3e-5, 13990d, 18600d);
ParametricFunction beta = new SterileNeutrinoSpectrum();
//ParametricFunction beta = new SterileNeutrinoSpectrum();
NBkgSpectrum spectrum = new NBkgSpectrum(beta);
XYModel model = new XYModel(spectrum, new SpectrumDataAdapter());

11
numass-main/src/main/java/inr/numass/models/LossCalculator.java
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@ -168,12 +168,12 @@ 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", x -> single.value(x), 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", x -> scatter.value(x), 0, 100, 1000));
}
UnivariateFunction total = (eps) -> {
@ -187,11 +187,11 @@ public class LossCalculator {
return sum;
};
frame.add(PlottableXYFunction.plotFunction("Total loss", x->total.value(x), 0, 100, 1000));
frame.add(PlottableXYFunction.plotFunction("Total loss", x -> total.value(x), 0, 100, 1000));
} else {
frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x->scatterFunction.value(x), 0, 100, 2000));
frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x -> scatterFunction.value(x), 0, 100, 2000));
}
}
@ -419,6 +419,9 @@ public class LossCalculator {
* @return
*/
public double getTotalLossValue(double X, double Ei, double Ef) {
if (X == 0) {
return 0;
}
List<Double> probs = getLossProbabilities(X);
double sum = 0;

8
numass-main/src/main/java/inr/numass/models/sterile/NumassTransmission.java
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@ -50,9 +50,13 @@ public class NumassTransmission extends AbstractParametricBiFunction {
return true;
}
private double getX(double eIn, NamedValueSet set) {
public static double getX(double eIn, NamedValueSet set) {
//From our article
return Math.log(eIn / ION_POTENTIAL) * eIn * ION_POTENTIAL / 1.9580741410115568e6;
return set.getDouble("X")*Math.log(eIn / ION_POTENTIAL) * eIn * ION_POTENTIAL / 1.9580741410115568e6;
}
public static double p0(double eIn, NamedValueSet set) {
return LossCalculator.instance().getLossProbability(0, getX(eIn, set));
}
@Override

63
numass-main/src/main/java/inr/numass/models/sterile/SterileNeutrinoSpectrum.java
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@ -12,8 +12,11 @@ import hep.dataforge.fitting.parametric.AbstractParametricFunction;
import hep.dataforge.fitting.parametric.ParametricBiFunction;
import hep.dataforge.meta.Meta;
import hep.dataforge.values.NamedValueSet;
import inr.numass.NumassContext;
import inr.numass.models.FSS;
import java.util.stream.DoubleStream;
import org.apache.commons.math3.analysis.BivariateFunction;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.distribution.EnumeratedRealDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.random.JDKRandomGenerator;
@ -29,9 +32,9 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
private static final String[] list = {"X", "trap", "E0", "mnu2", "msterile2", "U2"};
private double eMax;
private final RandomGenerator rnd;
private RealDistribution fssDistribution;
private FSS fss;
/**
* variables:Eo offset,Ein; parameters: "mnu2", "msterile2", "U2"
@ -50,9 +53,8 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
public SterileNeutrinoSpectrum(Context context, Meta configuration) {
super(list);
rnd = new SynchronizedRandomGenerator(new JDKRandomGenerator());
this.eMax = 18600;
if (configuration.hasValue("fssFile")) {
FSS fss = new FSS(context.io().getFile(configuration.getString("fssFile")));
fss = new FSS(context.io().getFile(configuration.getString("fssFile")));
fssDistribution = new EnumeratedRealDistribution(rnd, fss.getEs(), fss.getPs());
}
@ -82,13 +84,21 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
@Override
public double value(double u, NamedValueSet set) {
if (useDirect()) {
return integrateDirect(u, source, transmission, resolution, set);
} else {
return integrate(u, source, transmission, resolution, set);
}
}
private int numCalls(double u) {
return 100000;
}
private boolean useDirect() {
return true;
}
@Override
public boolean providesDeriv(String name) {
return source.providesDeriv(name) && transmission.providesDeriv(name) && resolution.providesDeriv(name);
@ -114,7 +124,13 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
ParametricBiFunction transmissionFunction,
ParametricBiFunction resolutionFunction,
NamedValueSet set) {
int num = numCalls(u);
double eMax = set.getDouble("E0") + 5d;
if (u > eMax) {
return 0;
}
double sum = DoubleStream.generate(() -> {
// generate final state
double fs;
@ -132,7 +148,7 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
* transmissionFunction.value(eIn, eOut, set)
* resolutionFunction.value(eOut, u, set);
if(Double.isNaN(res)){
if (Double.isNaN(res)) {
throw new Error();
}
return res;
@ -141,4 +157,43 @@ public class SterileNeutrinoSpectrum extends AbstractParametricFunction {
return Math.pow(eMax - u, 2d) / 2d * sum / num;
}
private double integrateDirect(
double u,
ParametricBiFunction sourceFunction,
ParametricBiFunction transmissionFunction,
ParametricBiFunction resolutionFunction,
NamedValueSet set) {
double eMax = set.getDouble("E0") + 5d;
if (u > eMax) {
return 0;
}
UnivariateFunction fsSource;
if (fss != null) {
fsSource = (eIn) -> {
double res = 0;
for (int i = 0; i < fss.size(); i++) {
res += fss.getP(i) * sourceFunction.value(fss.getE(i), u, set);
}
return res;
};
} else {
fsSource = (eIn) -> sourceFunction.value(0, eIn, set);
}
BivariateFunction transRes = (eIn, usp) -> {
UnivariateFunction integrand = (eOut) -> transmissionFunction.value(eIn, eOut, set) * resolutionFunction.value(eOut, usp, set);
return NumassContext.defaultIntegrator.integrate(integrand, usp, eIn);
};
UnivariateFunction integrand = (eIn) -> {
double p0 = NumassTransmission.p0(eIn, set);
return fsSource.value(eIn) * (p0 * resolutionFunction.value(eIn, u, set) + transRes.value(eIn, u));
};
return NumassContext.defaultIntegrator.integrate(integrand, u, eMax);
}
}