  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
of kT and anti-kT by preferring early association of soft radiation with a
resilient hard axis, while avoiding the specification of a fixed cone size.
Integrated filtering and variable-radius isolation criteria block assimilation
of soft wide-angle radiation and provide a halting condition. Mutually hard
structures are preserved to the end of clustering, automatically generating a
tree of subjet axis candidates. Excellent object identification and kinematic
reconstruction for multi-pronged resonances are realized across more than an
order of magnitude in transverse energy. The clustering measure history
facilitates high-performance substructure tagging, which we quantify with the
aid of supervised machine learning. These properties suggest that SIFT may
prove to be a useful tool for the continuing study of jet substructure.
