jhpro.stat

Class CLimits

java.lang.Object

jhpro.stat.CLimits

public class CLimits
extends java.lang.Object

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

Constructors

Constructor and Description
CLimits(DataSource data, int nmc) Main constructor.
CLimits(DataSource data, int nmc, boolean stat) Main constructor.
CLimits(H1D s, H1D b, H1D d, double se, double be, double de, java.lang.String l, int nmc) Initialize limit calculations for one channel with systematic errors.
CLimits(H1D s, H1D b, H1D d, int nmc) Get limits including statistical errors in the calculations.
CLimits(P1D s, P1D b, P1D d, int nmc) Initialize limit calculations including statistical errors in the calculations.

Method Summary

Modifier and Type	Method and Description
void	doc() Show online documentation.
ConfidenceLevel	getLimit() Build a Confidence level.
ConfidenceLevel	getLimit(java.util.Random generator) Build a Confidence level with a custom Random() to build Poisson random numbers.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CLimits

public CLimits(DataSource data,
               int nmc)

Main constructor. Takes the signal, background and data histograms as well as different systematics sources to form the limit input. For this, nmc Monte Carlo experiments are performed. As usual, the larger this number, the longer the compute time, but the better the result. Statistical errors are included in the calculations.

Parameters:

data - input data (signal, background, data)

nmc - Number of MC experiments. Usually 50000.

CLimits

public CLimits(DataSource data,
               int nmc,
               boolean stat)

Main constructor. Takes the signal, background and data histograms as well as different systematics sources to form the limit input. For this, nmc Monte Carlo experiments are performed. As usual, the larger this number, the longer the compute time, but the better the result.

Parameters:

data - input data (signal, background, data)

nmc - Number of MC experiments. Usually 50000.

stat - set true if statistical errors are included in the estimate

CLimits

public CLimits(H1D s,
               H1D b,
               H1D d,
               int nmc)

Get limits including statistical errors in the calculations. Statistical errors are included.

Parameters:

s - signal

b - background

d - data

nmc - number of MC points

CLimits

public CLimits(P1D s,
               P1D b,
               P1D d,
               int nmc)

Initialize limit calculations including statistical errors in the calculations.

Parameters:

s - signal

b - background

d - data

nmc - number of MC points

CLimits

public CLimits(H1D s,
               H1D b,
               H1D d,
               double se,
               double be,
               double de,
               java.lang.String l,
               int nmc)

Initialize limit calculations for one channel with systematic errors. Statistical errors are included. One can define systematic error. Example, the number 0.05 means scaling by 1+0.05 (5% systematic).

Parameters:

s - Signal histogram

b - Background histogram

d - Data histogram

se - Systematic error on signal

be - Systematic error on background

de - Systematic errors on data

l - Name of this channel

nmc - Number of MC histograms

Method Detail

getLimit

public ConfidenceLevel getLimit()

Build a Confidence level.

Returns:

getLimit

public ConfidenceLevel getLimit(java.util.Random generator)

Build a Confidence level with a custom Random() to build Poisson random numbers.

Parameters:

stat - usually false

generator - . Usually Random(). Used to build Poisson random numbers.

Returns:

doc

public void doc()

Show online documentation.