A normalizing-flow neural network reconstructs the momenta of the neutrino and dark mediator in single top quark production and reproduces a top-quark spin-correlation variable more accurately than a multilayer perceptron.
Reconstruction of angular correlations in the associated top quark and the dark matter mediator production
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abstract
For the process of single top quark production within the "simplified model" with a scalar dark matter mediator, a new variable based on angular correlations was presented, for the proper reconstruction of which it is necessary to separate the contributions of two undetectable particles: the neutrino and the mediator. In this work, various machine learning approaches for reconstructing the momenta of these particles are analyzed. A comparison is made between the results obtained using a multilayer perceptron and the Normalizing Flows architectures. The neural networks based on Normalizing Flows, presented in this work, demonstrate a high quality of reconstruction of the target variable and can be used for collider data analysis.
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Reconstruction of angular correlations in the associated top quark and the dark matter mediator production
A normalizing-flow neural network reconstructs the momenta of the neutrino and dark mediator in single top quark production and reproduces a top-quark spin-correlation variable more accurately than a multilayer perceptron.