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java.lang.Objectjhpro.stat.CLimits
public class CLimits
Algorithm to compute 95% CL limits using the Likelihood ratio semi-Bayesian method. It takes signal, background and data histograms wrapped in a DataSource as input and runs a set of Monte Carlo experiments in order to compute the limits.
When excluding new physics models, 95% CL exclusion is usually the criterion chosen, and one only has to compute CLs with enough precision to tell that an observed outcome is less probable than about 5% of the time assuming a signal is present. But forming discovery p-values, we must compute 1-CLb values of the order of 1 10^{-7}. This computation involves generating of the order of 1 10^{8} pseudoexperiments, just to be on the safe side. If needed, inputs are fluctuated according to systematics.
Reference: HEP-EX/9902006. see: Tom Junk,NIM A434, p. 435-443, 1999 see http://root.cern.ch/root/doc/TomJunk.pdf
Constructor Summary | |
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CLimits(DataSource data,
int nmc)
Main constructor. |
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CLimits(DataSource data,
int nmc,
boolean stat)
Main constructor. |
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CLimits(H1D s,
H1D b,
H1D d,
double se,
double be,
double de,
String l,
int nmc)
Initialize limit calculations for one channel with systematic errors. |
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CLimits(H1D s,
H1D b,
H1D d,
int nmc)
Get limits including statistical errors in the calculations. |
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CLimits(P1D s,
P1D b,
P1D d,
int nmc)
Initialize limit calculations including statistical errors in the calculations. |
Method Summary | |
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void |
doc()
Show online documentation. |
ConfidenceLevel |
getLimit()
Build a Confidence level. |
ConfidenceLevel |
getLimit(Random generator)
Build a Confidence level with a custom Random() to build Poisson random numbers. |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public CLimits(DataSource data, int nmc)
data
- input data (signal, background, data)nmc
- Number of MC experiments. Usually 50000.public CLimits(DataSource data, int nmc, boolean stat)
data
- input data (signal, background, data)nmc
- Number of MC experiments. Usually 50000.stat
- set true if statistical errors are included in the estimatepublic CLimits(H1D s, H1D b, H1D d, int nmc)
s
- signalb
- backgroundd
- datanmc
- number of MC pointspublic CLimits(P1D s, P1D b, P1D d, int nmc)
s
- signalb
- backgroundd
- datanmc
- number of MC pointspublic CLimits(H1D s, H1D b, H1D d, double se, double be, double de, String l, int nmc)
s
- Signal histogramb
- Background histogramd
- Data histogramse
- Systematic error on signalbe
- Systematic error on backgroundde
- Systematic errors on datal
- Name of this channelnmc
- Number of MC histogramsMethod Detail |
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public ConfidenceLevel getLimit()
public ConfidenceLevel getLimit(Random generator)
stat
- usually falsegenerator
- . Usually Random(). Used to build Poisson random numbers.
public void doc()
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