Speaker
Anna Benecke
(UC Louvain)
Description
We present results using an optimized jet clustering with variable $R$, where the jet distance parameter $R$ depends on the mass and transverse momentum $p_T$ of the jet. The jet size decreases with increasing $p_T$, and increases with increasing mass. This choice is motivated by the kinematics of hadronic decays of highly Lorentz boosted top quarks, W, Z, and H bosons. The jet clustering features an inherent grooming with soft drop and a reconstruction of subjets in one sequence. These features have been implemented in the Heavy Object Tagger with Variable R (HOTVR) algorithm, which we use to study the performance of jet substructure tagging with different choices of grooming parameters and functional forms of $R$.
