CLimits
jhpro.stat

Class CLimits



  • public class CLimitsextends 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 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,       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(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.

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