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Confidence level computation for combining searches with small statistics
| Content Provider | Semantic Scholar |
|---|---|
| Author | Thomas Junk |
| Copyright Year | 1999 |
| Abstract | This article describes an efficient procedure for computing approximate confidence levels for searches for new particles where the expected signal and background levels are small enough to require the use of Poisson statistics. The results of many independent searches for the same particle may be combined easily, regardless of the discriminating variables which may be measured for the candidate events. The effects of systematic uncertainty in the signal and background models are incorporated in the confidence levels. The procedure described allows efficient computation of expected confidence levels. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.hep.princeton.edu/~mcdonald/examples/detectors/junk_nim_a434_435_99.pdf |
| Alternate Webpage(s) | http://cds.cern.ch/record/378576/files/9902006.pdf |
| Alternate Webpage(s) | http://arxiv.org/pdf/hep-ex/9902006v1.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Computation (action) Computational complexity theory Consistency model Electronic signature Ewald summation Exclusion Experiment Leucaena pulverulenta Monte Carlo method Simulation Statistic (data) Summation (document) |
| Content Type | Text |
| Resource Type | Article |
