Identifying dark matter interactions in monojet searches
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Michigan State Univ., East Lansing, MI (United States)
We study the discrimination of quark-initiated jets from gluon-initiated jets in monojet searches for dark matter using the technique of averaged jet energy profiles. We demonstrate our results in the context of effective field theories of dark matter interactions with quarks and gluons, but our methods apply more generally to a wide class of models. Different effective theories of dark matter and the standard model backgrounds each have a characteristic quark/gluon fraction for the leading jet. When used in conjunction with the traditional cut-and-count monojet search, the jet energy profile can be used to set stronger bounds on contact interactions of dark matter. In the event of a discovery of a monojet excess at the 14 TeV LHC, contact interactions between dark matter with quarks or with gluons can be differentiated at the 95% confidence level. For a given rate at the LHC, signal predictions at direct detection experiments for different dark matter interactions can span five orders of magnitude. Lastly, the ability to identify these interactions allows us to make a tighter connection between LHC searches and direct detection experiments.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1155853
- Report Number(s):
- MSU-16122013; arXiv eprint number arXiv:1312.5325
- Journal Information:
- Journal of High Energy Physics (Online), Vol. 2014, Issue 5; ISSN 1029-8479
- Publisher:
- Springer BerlinCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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journal | October 2018 |
Distinguishing Di-jet Resonances at the LHC | text | January 2014 |
Probing the Higgs boson-gluon coupling via the jet energy profile at $e^+e^-$ colliders
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text | January 2018 |
Quark-Gluon Tagging: Machine Learning vs Detector | text | January 2018 |
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