Abstract
New particles beyond the Standard Model might be produced with a very high boost, for instance if they result from the decay of a heavier particle. If the former decay hadronically, then their signature is a single massive fat jet which is difficult to separate from QCD backgrounds. Jet substructure and machine learning techniques allow for the discrimination of many specific boosted objects from QCD, but the scope of possibilities is very large, and a suite of dedicated taggers may not be able to cover every possibility — in addition to making experimental searches cumbersome. In this paper we describe a generic model-independent tagger that is able to discriminate a wide variety of hadronic boosted objects from QCD jets using N -subjettiness variables, with a significance improvement varying between 2 and 8. This is in addition to any improvement that might come from a cut on jet mass. Such a tagger can be used in model-independent searches for new physics yielding fat jets. We also show how such a tagger can be applied to signatures over a wide range of jet masses without sculpting the background distributions, allowing to search for new physics as bumps on jet mass distributions.
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