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Kernel Estimation in High-Energy Physics
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Kernel Estimation provides an unbinned and non-parametric estimate of the probability density function from which a set of data is drawn. In the first section, after a brief discussion on parametric and non-parametric methods, the theory of Kernel Estimation is developed for univariate and multivariate settings. The second section discusses some of the applications of Kernel Estimation to high-energy physics. The third section provides an overview of the available univariate and multivariate packages. This paper concludes with a discussion of the inherent advantages of kernel estimation techniques and systematic errors associated with the estimation of parent distributions.
Forward citations
Cited by 3 Pith papers
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Observation of a $B_c^{*+}$ meson with the ATLAS detector
First observation of the B_c^{*+} meson with mass difference 64.5 MeV from the ground-state B_c^+ at >8 sigma significance using ATLAS data.
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Searches for $B^0\to K^+\pi^-\tau^+\tau^-$ and $B_s^0\to K^+K^-\tau^+\tau^-$ decays
LHCb reports the first upper limits on B0 → K+π−τ+τ− and Bs0 → K+K−τ+τ− branching fractions, with recast limits of 2.8×10−4 on B0 → K*(892)0 τ+τ− at 95% CL that improve prior bounds by an order of magnitude.
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Charged-lepton identification at Belle~II
Belle II describes its charged-lepton identification algorithms and reports performance on 428 fb^{-1} of Run 1 data from SuperKEKB.
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