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Register a new userMeasuring the polarization of boosted, hadronic $W$ bosons with jet substructure observables
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In this work, we present a new technique to measure the longitudinal and transverse polarization fractions of hadronic decays of boosted $W$ bosons. We introduce a new jet substructure observable denoted as $p_theta$, which is a proxy for the parton level decay polar angle of the $W$ boson in its rest-frame. We show that the distribution of this observable is sensitive to the polarization of $W$ bosons and can therefore be used to reconstruct the $W$ polarization in a model-independent way. As a test case, we study the efficacy of our technique on vector boson scattering processes at the high luminosity Large Hadron Collider and we find that our technique can determine the longitudinal polarization fraction to within $pm 0.15$. We also show that our technique can be used to identify the parity of beyond Standard Model scalar or pseudo-scalar resonances decaying to $W$ bosons with just 20 events.
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A method is proposed for distinguishing highly boosted hadronically decaying Ws (W-jets) from QCD-jets using jet substructure. Previous methods, such as the filtering/mass-drop method, can give a factor of ~2 improvement in S/sqrt(B) for jet pT > 200 GeV. In contrast, a multivariate approach including new discriminants such as R-cores, which characterize the shape of the W-jet, subjet planar flow, and grooming-sensitivities is shown to provide a much larger factor of ~5 improvement in S/sqrt(B). For longitudinally polarized Ws, such as those coming from many new physics models, the discrimination is even better. Comparing different Monte Carlo simulations, we observe a sensitivity of some variables to the underlying event; however, even with a conservative estimates, the multivariate approach is very powerful. Applications to semileptonic WW resonance searches and all-hadronic W+jet searches at the LHC are also discussed. Code implementing our W-jet tagging algorithm is publicly available at http://jets.physics.harvard.edu/wtag
Top polarization is an important probe of new physics that couples to the top sector, and which may be discovered at the 14 TeV LHC. Taking the example of the MSSM, we argue that top polarization measurements can put a constraint on the soft supersymmetry breaking parameter A_t. In light of the recent discovery of a Higgs-like boson of mass ~125 GeV, a large A_t is a prediction of many supersymmetric models. To this end, we develop a *detector level* analysis methodology for extracting polarization information from hadronic tops using boosted jet substructure. We show that with 100 fb^(-1) of data, left and right 600 GeV stops can be distinguished to 4sigma, and 800 GeV stops can be distinguished to 3sigma.
A number of recent applications of jet substructure, in particular searches for light new particles, require substructure observables that are decorrelated with the jet mass. In this paper we introduce the Convolved SubStructure (CSS) approach, which uses a theoretical understanding of the observable to decorrelate the complete shape of its distribution. This decorrelation is performed by convolution with a shape function whose parameters and mass dependence are derived analytically. We consider in detail the case of the $D_2$ observable and perform an illustrative case study using a search for a light hadronically decaying $Z$. We find that the CSS approach completely decorrelates the $D_2$ observable over a wide range of masses. Our approach highlights the importance of improving the theoretical understanding of jet substructure observables to exploit increasingly subtle features for performance.
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.
This report of the BOOST2012 workshop presents the results of four working groups that studied key aspects of jet substructure. We discuss the potential of the description of jet substructure in first-principle QCD calculations and study the accuracy of state-of-the-art Monte Carlo tools. Experimental limitations of the ability to resolve substructure are evaluated, with a focus on the impact of additional proton proton collisions on jet substructure performance in future LHC operating scenarios. A final section summarizes the lessons learnt during the deployment of substructure analyses in searches for new physics in the production of boosted top quarks.
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