  N-subjettiness is a jet shape designed to identify boosted hadronic objects
such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the
angular distances of jet constituents to their nearest subjet axis. Here, we
generalize and improve on N-subjettiness by minimizing over all possible subjet
directions, using a new variant of the k-means clustering algorithm. On boosted
top benchmark samples from the BOOST2010 workshop, we demonstrate that a simple
cut on the 3-subjettiness to 2-subjettiness ratio yields 20% (50%) tagging
efficiency for a 0.23% (4.1%) fake rate, making N-subjettiness a highly
effective boosted top tagger. N-subjettiness can be modified by adjusting an
angular weighting exponent, and we find that the jet broadening measure is
preferred for boosted top searches. We also explore multivariate techniques,
and show that additional improvements are possible using a modified Fisher
discriminant. Finally, we briefly mention how our minimization procedure can be
extended to the entire event, allowing the event shape N-jettiness to act as a
fixed N cone jet algorithm.
