Boosting mono-jet searches with model-agnostic machine learning
arXiv:2204.11889 · doi:10.1007/JHEP08(2022)015
Abstract
We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably.
arXiv comment19 pages, 3 figures. v2: references added
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