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The impact of event colour structure on the performance of the Johns-Hopkins, CMS, HEPToptagger and N-Subjettiness algorithms is investigated by studying colour singlet and colour octet resonances decaying to top-quark pairs. Large differences in top-tagging efficiency are observed due to the different colour charge of each resonance. These differences are quantified as a function of the algorithm parameters, the jet size parameter and the probability to misidentify light quarks and gluons as top candidates. We suggest that future experimental searches would benefit from optimising the choice of algorithm parameters in order to minimise this source of model dependency.
We evaluate the phenomenological applicability of the dynamical grooming technique, introduced in [1], to boosted W and top tagging at LHC conditions. An extension of our method intended for multi-prong decays with an internal mass scale, such as the
A method is proposed for distinguishing highly boosted hadronically decaying Ws (W-jets) from QCD-jets using jet substructure. Previous methods, such as the filtering/mass-drop method, can give a factor of ~2 improvement in S/sqrt(B) for jet pT > 200
Jet identification tools are crucial for new physics searches at the LHC and at future colliders. We introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wid
Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditio
Models that produce a flux of semi-relativistic or relativistic boosted dark matter at large neutrino detectors are well-motivated extensions beyond the minimal weakly interacting massive particle (WIMP) paradigm. Current and upcoming liquid argon ti