Performance of the CMS missing transverse energy reconstruction in pp data at sqrt(s) = 8 TeV
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The performance of missing transverse energy reconstruction algorithms is presented using sqrt(s) = 8 TeV proton-proton (pp) data collected with the CMS detector. Events with anomalous missing transverse energy are studied, and the performance of algorithms used to identify and remove these events is presented. The scale and resolution for missing transverse energy, including the effects of multiple pp interactions (pileup), are measured using events with an identified Z boson or isolated photon, and are found to be well described by the simulation. Novel missing transverse energy reconstruction algorithms developed specifically to mitigate the effects of large numbers of pileup interactions on the missing transverse energy resolution are presented. These algorithms significantly reduce the dependence of the missing transverse energy resolution on pileup interactions. Finally, an algorithm that provides an estimate of the significance of the missing transverse energy is presented, which is used to estimate the compatibility of the reconstructed missing transverse energy with a zero nominal value.
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Forward citations
Cited by 1 Pith paper
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DeepMET: Improving missing transverse momentum estimation with a deep neural network
DeepMET is a neural-network-based missing transverse momentum estimator that improves resolution by 10-30% over existing CMS methods across a range of final states.
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