Jet clustering with a scale-invariant filtered tree: A new algorithm for the substructure era

We introduce a new jet clustering algorithm named SIFT (scale-invariant filtered tree) that maintains the resolution of substructure for collimated decay products at large boosts. The scale-invariant measure combines properties ${k}_{\mathrm{T}}$ and anti-${k}_{\mathrm{T}}$ by preferring early association soft radiation with resilient hard axis, while avoiding specification fixed cone size. Integrated filtering variable-radius isolation criteria block assimilation wide-angle provide halting condition. Mutually structures are preserved to end clustering, automatically generating tree subjet axis candidates. Excellent object identification kinematic reconstruction multipronged resonances realized across more than an order magnitude in transverse energy. history facilitates high-performance tagging, which we quantify aid supervised machine learning. These suggest may prove be useful tool continuing study substructure.