SPlot: A Statistical tool to unfold data distributions

By M. Pivk, F. L. Diberder. 2004

1,287 citations recorded by the source corpus.

Abstract The paper advocates the use of a statistical tool dedicated to the exploration of data samples populated by several sources of events. This new technique, called P lot s , is able to unfold the contributions of the different sources to the distribution of a data sample in a given variable. The P lot s tool applies in the context of a likelihood fit which is performed on the data sample to determine the yields of the various sources.

Subfields: Artificial Intelligence, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/fe59fe86ff0f7b4175e493d2dfb5deb3920cc978

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Citations in the corpus

Observation of X(3872) production in pp collisions at root s=7 TeV (2011, positive): “A second method based on the sWeight [29] technique was found to give consistent results.”
Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at √s=8 TeV (2015, methodology): “The remnant background is subtracted statistically, using the sPlot technique [38], through a fit to the dielectron invariant mass.”
First observation of the decay Bs0 → KS0K∗(892)0 at LHCb (2015, methodology): “Using the sPlot technique [30], the cos θK∗0 distribution of the signal P and S wave is unfolded from the other background components.”
Measurement of CP asymmetry in D0 → K−K+ and D0 → π−π+ decays (2014, neutral): “In this procedure, the average decay time of the signal in each sample is determined by subtracting the decay time distributions of background events using the sPlot technique [27], and by correcting for decay time resolution eﬀects.”
The PIDCalib package (2016, methodology): “The use of sWeights relies on the assumptions of the sP lot formalism [9], namely that the parameterising variables are uncorrelated to the fit variables (usually the mass of the vertex).”
Exotics: Heavy pentaquarks and tetraquarks (2017, methodology): “Candidates are assigned weights using the sPlot technique [127] allowing the signal and background components to be projected out depending on their J/ψpπ− mass.”
Performance of CMS muon reconstruction in pp collision events at √s = 7 TeV (2012, methodology): “For J/ψ events, the kinematic distributions of the signal probes are extracted from the distributions of all probes by using the SPlot technique [29].”
Measurement of the $J/\psi$ pair production cross-section in $pp$ collisions at $\sqrt{s} = 13 \,{\mathrm{TeV}}$ (2016, methodology): “This is estimated by comparing the vertex-ﬁt quality of the reconstructed J/ψ candidates between the simulated and the data samples, where the background is subtracted using the sPlot technique [88].”
Study of J/ψ production and cold nuclear matter effects in pPb collisions at $ \sqrt{{{s_{NN }}}} $ = 5 TeV (2013, methodology): “The tz distribution of background in each kinematic bin is independently modelled with an empirical function based on the tz distribution observed in background events obtained using the sPlot technique [30].”
Opportunities in Flavour Physics at the HL-LHC and HE-LHC (2018, methodology): “The signal lifetime distribution for each pseudo-experiment is obtained using the sPlot technique [1095] to separate out the background, and then fitted with a model consisting of an exponential function, convolved with a Gaussian function that describes the expected decay time resolution, and multi”
Measurements of the properties of the Higgs-like boson in the four lepton decay channel with the ATLAS detector using 25 fb$^{-1}$ of proton-proton collision data (2013, methodology): “The sP lot method [50] is used to unfold the contributions from the different background sources as a function of electron pT .”
Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV (2012, methodology): “For J/ψ events, the kinematic distributions of the signal probes are extracted from the distributions of all probes by using the SPlot technique [29].”
Measurements of prompt charm production cross-sections in pp collisions at s√=5TeV (2016, methodology): “The signal distributions for this weighting are determined with the sPlot technique [31] with lnχ(2)IP as the discriminating variable, where χ(2)IP is defined as the difference in χ 2 of the PV reconstructed with and without the particle under consideration.”
Test of lepton universality with B0 → K*0ℓ+ℓ− decays (2017, methodology): “The sPlot technique [57], where m(K+π−`+`−) is used as the discriminating variable, is adopted to subtract statistically the background from the B0→ K∗0`+`− selected data, and test the agreement between muons and electrons, data and simulation, using several control quantities (see figure 8): the q2”