How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics

Details How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics

2010-06-15

arxiv.org/abs/1006.3019v2

JINST 5:P09004,2010

Physics

High Energy Physics

High Energy Physics - Experiment

32 pages, 12 figures

Scientific paper

10.1088/1748-0221/5/09/P09004

Multivariate analyses play an important role in high energy physics. Such analyses often involve performing an unbinned maximum likelihood fit of a probability density function (p.d.f.) to the data. This paper explores a variety of unbinned methods for determining the goodness of fit of the p.d.f. to the data. The application and performance of each method is discussed in the context of a real-life high energy physics analysis (a Dalitz-plot analysis). Several of the methods presented in this paper can also be used for the non-parametric determination of whether two samples originate from the same parent p.d.f. This can be used, e.g., to determine the quality of a detector Monte Carlo simulation without the need for a parametric expression of the efficiency.

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