Reconstruction of lepton pair invariant mass using an artificial neural network
arXiv:1904.04924 · doi:10.1016/j.nima.2019.03.029
Abstract
The reconstruction of the invariant mass of lepton pairs is important for analyses containing Higgs and Z bosons decaying to , but highly challenging due to the neutrinos from the lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural networks can be used to reconstruct the mass of a di- system and compare this procedure to an algorithm used by the CMS Collaboration for this purpose. We find that the neural network output shows a smaller bias and better resolution of the di- mass reconstruction and an improved discrimination between a Higgs boson signal and the Drell-Yan background with a much shorter computation time.
arXiv comment11 pages, 7 figures
References in corpus (14)
- Adam: A Method for Stochastic Optimization
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- PYTHIA 6.4 Physics and Manual
- PYTHIA 6.2 Physics and Manual
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Introduction to PYTHIA 8.2
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Observation of the Higgs boson decay to a pair of tau leptons with the CMS detector
- A New Mass Reconstruction Technique for Resonances Decaying to di-tau
- Higgs production at the lHC
- Cross-section measurements of the Higgs boson decaying into a pair of tau-leptons in proton-proton collisions at TeV with the ATLAS detector
- Reconstruction of the Higgs mass in events with Higgs bosons decaying into a pair of tau leptons using matrix element techniques