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Register a new userInvestigation of Monte Carlo Uncertainties on Higgs Boson searches using Jet Substructure
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We present an investigation of the dependence of searches for boosted Higgs bosons using jet substructure on the perturbative and non-perturbative parameters of the Herwig++ Monte Carlo event generator. Values are presented for a new tune of the parameters of the event generator, together with the an estimate of the uncertainties based on varying the parameters around the best-fit values.
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We present an update of the branching ratios for Higgs-boson decays in the Standard Model. We list results for all relevant branching ratios together with corresponding uncertainties resulting from input parameters and missing higher-order corrections. As sources of parametric uncertainties we include the masses of the charm, bottom, and top quarks as well as the QCD coupling constant. We compare our results with other predictions in the literature.
In this short review we discuss two implementations of the charged Higgs boson production process in association with a top quark in Monte Carlo event generators at next-to-leading order in QCD. We introduce the MC@NLO and the POWHEG method of matching next-to-leading order matrix elements with parton showers and compare both methods analyzing the charged Higgs boson production process in association with a top quark. We shortly discuss the case of a light charged Higgs boson where the associated charged Higgs production interferes with the charged Higgs production via t tbar-production and subsequent decay of the top quark.
The search for di-Higgs final states is typically limited at the LHC to the dominant gluon fusion channels, with weak boson fusion only assuming a spectator role. In this work, we demonstrate that when it comes to searches for resonant structures that arise from iso-singlet mixing in the Higgs sector, the weak boson fusion sideline can indeed contribute to winning the discovery game. Extending existing experimental resonance searches by including both contributions is therefore crucial.
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.
It is widely considered that, for Higgs boson searches at the Large Hadron Collider, WH and ZH production where the Higgs boson decays to b anti-b are poor search channels due to large backgrounds. We show that at high transverse momenta, employing state-of-the-art jet reconstruction and decomposition techniques, these processes can be recovered as promising search channels for the standard model Higgs boson around 120 GeV in mass.
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