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Jet substructure is typically studied using clustering algorithms, such askT, which arrange the jets' constituents into trees. Instead of considering asingle tree per jet, we propose that multiple trees should be considered,weighted by an appropriate metric. Then each jet in each event produces adistribution for an observable, rather than a single value. Advantages of thisapproach include: 1) observables have significantly increased statisticalstability; and, 2) new observables, such as the variance of the distribution,provide new handles for signal and background discrimination. For example, wefind that employing a set of trees substantially reduces the observedfluctuations in the pruned mass distribution, enhancing the likelihood of newparticle discovery for a given integrated luminosity. Furthermore, theresulting pruned mass distributions for (background) QCD jets are found to besubstantially wider than that for (signal) jets with intrinsic mass scales,e.g. jets containing a W decay. A cut on this width yields a substantialenhancement in significance relative to a cut on the standard pruned jet massalone. In particular the luminosity needed for a given significance requirementdecreases by a factor of two relative to standard pruning.

Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure