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

Abstract

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.

32 pages, 12 figures

Cited by in corpus (15)

• Amplitude analysis of $B^{-}\to D^{+}{\pi }^{-}{\pi }^{-}$ decays
• Amplitude analysis of e+e- => Y(nS)pi+pi- at sqrt(s)=10.865 GeV
• Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density
• Mixing and $CP$ violation in the charm system
• Anomaly detection from mass unspecific jet tagging
• Amplitude analysis of $B_s^0\to K_{\text{S}}^0{K}^{\pm }{\pi }^{\mp }$ decays
• Search for $CP$ violation in ${\Xi }_b^{-}\to p{K}^{-}{K}^{-}$ decays
• Observation of a New $X(3872)$ Production Process $e^{+}{e}^{-}\to \omega X(3872)$
• Determination of short- and long-distance contributions in $B^0\to K^{\ast 0}{\mu }^{+}{\mu }^{-}$ decays
• Goodness of fit by Neyman-Pearson testing
• Amplitude Analysis and Branching Fraction Measurement of $D^0\to K^{-}{\pi }^{+}{\pi }^0{\pi }^0$
• CP Violation In Baryon Decays At LHCb
• Amplitude analysis of the $D^{+}\to {\pi }^{-}{\pi }^{+}{\pi }^{+}$ decay and measurement of the ${\pi }^{-}{\pi }^{+}$ S-wave amplitude
• A Practical Guide to Unbinned Unfolding
• Data-Driven High-Dimensional Statistical Inference with Generative Models