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Spot the stop with a b-tag

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 Added by Riccardo Torre
 Publication date 2015
  fields
and research's language is English




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The LHC searches for light compressed stop squarks have resulted in considerable bounds in the case where the stop decays to a neutralino and a charm quark. However, in the case where the stop decays to a neutralino, a bottom quark and two fermions via an off-shell W-boson, there is currently a significant unconstrained region in the stop-neutralino mass plane, still allowing for stop masses in the range 90-140 GeV. In this paper we propose a new monojet-like search for light stops, optimized for the four-body decay mode, in which at least one $b$-tagged jet is required. We show that, already by using the existing 8 TeV LHC data set, such a search would cover the entire unconstrained region. Moreover, in the process of validating our tools against an ATLAS monojet search, we show that the existing limit can be extended to exclude also stop masses below 100 GeV.



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A significant part of the parameter space for light stop squarks still remains unconstrained by collider searches. For both R-Parity Conserving (RPC) and R-Parity Violating (RPV) scenarios there are regions in which the stop mass is around or below the top quark mass that are particularly challenging experimentally. Here we review the status of light stop searches, both in RPC and RPV scenarios. We also propose strategies, generally based on exploiting b-tagging, to cover the unconstrained regions.
The top squarks (stops) may be the most wanted particles after the Higgs boson discovery. The searches for the lightest stop have put strong constraints on its mass. However, there is still a search gap in the low mass region if the spectrum of the stop and the lightest neutralino is compressed. In that case, it may be easier to look for the second stop since naturalness requires both stops to be close to the weak scale. The current experimental searches for the second stop are based on the simplified model approach with the decay modes $tilde{t}_2 to tilde{t}_1 Z$ and $tilde{t}_2 to tilde{t}_1 h$. However, in a realistic supersymmetric spectrum there is always a sbottom lighter than the second stop, hence the decay patterns are usually more complicated than the simplified model assumptions. In particular, there are often large branching ratios of the decays $tilde{t}_2 to tilde{b}_1 W$ and $tilde{b}_1 to tilde{t}_1 W$ as long as they are open. The decay chains can be even more complex if there are intermediate states of additional charginos and neutralinos in the decays. By studying several MSSM benchmark models at the 14 TeV LHC, we point out the importance of the multi-$W$ final states in the second stop and the sbottom searches, such as the same-sign dilepton and multilepton signals, aside from the traditional search modes. The observed same-sign dilepton excesses at LHC Run 1 and Run 2 may be explained by some of our benchmark models. We also suggest that the vector boson tagging and a new kinematic variable may help to suppress the backgrounds and increase the signal significance for some search channels. Due to the complex decay patterns and lack of the dominant decay channels, the best reaches likely require a combination of various search channels at the LHC for the second stop and the lightest sbottom.
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Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a digital image of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p_T in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.
The production of a W boson and two jets, at least one of which contains a b quark, is a principal background to single-top production, Higgs production, and signals of new physics at hadron colliders. We present a next-to-leading-order (NLO) calculation of the cross section at the Fermilab Tevatron and the CERN Large Hadron Collider. The NLO cross section differs substantially from that at LO, and we provide a context in which to understand this result.
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