Augmenting collider searches and enhancing discovery potentials through stochastic jet grooming

Abstract

Abstract:The jet Trimming procedure has been demonstrated to greatly improve event reconstruction in hadron collisions, by mitigating contamination due initial state radiation, multiple interactions, and event pileup. Meanwhile, Qjets -a nondeterministic approach to tree-based jet substructure has been shown to be a powerful technique in decreasing random statistical fluctuations, yielding significant effective luminosity improvements. This manifests through an improvement in the significance S/δB, relative to conventional methods. Qjets also provide novel observables in many cases, like mass-volatility, that could be used to further discriminate between signal and background events. The statistical robustness and volatility observables, for tagging, are obtained simultaneously. We explore here a combination of the two techniques, and demonstrate that significant enhancements in discovery potentials may be obtained in non-trivial ways. We will illustrate this by considering a diboson resonance analysis as a case study -enabling us to interpolate between scenarios where the gains are purely due to statistical robustness and scenarios where the gains are also reinforced by volatility variable discriminants. The former, for instance, is applicable to digluon/diquark resonances, while the latter will be of relevance to di-W ± /di-Z 0 resonances, where the boosted vector bosons are decaying hadronically and have an intrinsic mass scale attached to them. We argue that one can enhance signal significance and discovery potentials markedly through stochastic grooming, and help augment studies at the Large Hadron Collider and future hadron colliders.

Looking for minimal inverse seesaw scenarios at the LHC with jet substructure techniques

“…As mentioned, the lack of any unambiguous indications of new physics at collider and non-collider experiments is perplexing though. This has recently reinvigorated searches for novel methodologies and paradigms, for instance, to categorize anomalous objects [24], or efficiently groom jets either stochastically [25] or using the unparalleled power of machine learning [26]. Please see [27,28] and references therein for detailed discussions.…”