A deep neural network-based tagger to search for new long-lived particle states decaying to jets

Collaboration
2019
25 pages
Report number:
  • CMS-PAS-EXO-19-011
Experiments:

Citations per year

20182019202001
Abstract:
The development of a tagging algorithm to identify jets that are significantly displaced from the luminous regions of LHC proton-proton (pp) collisions is presented. Displaced jets can arise from the decay of a long-lived particle (LLP), which are predicted by several theoretical extensions to the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterized according to the proper decay length cτ0\text{c}\tau_0 of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of both simulated events and pp collision data are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The tagger is applied in a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 1000010\,000 for jets from standard model processes while maintaining an LLP jet tagging efficiency of 30{-}80%\% for split supersymmetric models with 1mmcτ010m1\,\text{mm} \leq \text{c}\tau_0 \leq 10\,\text{m}. The expected coverage of the split supersymmetric model parameter space is presented.
Note:
  • Preliminary results
  • Monte-Carlo
  • supersymmetry: split
  • particle: long-lived
  • hadronic decay
  • gluino: long-lived
  • p p: scattering
  • decay: length
  • neural network
  • performance
  • CERN LHC Coll