A deep neural network to search for new long-lived particles decaying to jets
- Yerevan Physics Institute, Yerevan (Armenia); CMS Collaboration. et al.
A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length cτ0 of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events 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 potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split super symmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30–80% for gluinos with 1 mm≤ cτ0 ≤10 m. The expected coverage of the parameter space for split super-symmetry is presented.
- Research Organization:
- Univ. of Colorado, Boulder, CO (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- CMS Collaboration
- Grant/Contract Number:
- SC0010005; AC02-05CH11231; AC02-07CH11359
- OSTI ID:
- 1721479
- Alternate ID(s):
- OSTI ID: 1596058; OSTI ID: 1870123
- Report Number(s):
- FERMILAB-PUB-20-039-CMS; CMS-EXO-19-011; CERN-EP-2019-281; arXiv:1912.12238; TRN: US2204829
- Journal Information:
- Machine Learning: Science and Technology, Vol. 1, Issue 3; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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