50 research outputs found

    Measuring quark polarizations at ATLAS and CMS

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    Being able to measure the polarization of quarks produced in various processes at the LHC would be of fundamental significance. Measuring the polarizations of quarks produced in new physics processes, once discovered, can provide crucial information about the new physics Lagrangian. In a series of recent papers, we have investigated how quark polarization measurements can be done in practice. The polarizations of heavy quarks (b and c) are expected to be largely preserved in the lightest baryons they hadronize into, the Lambda_b and Lambda_c, respectively. Furthermore, it is known experimentally that s-quark polarization is preserved as well, in Lambda baryons. We study how ATLAS and CMS can measure polarizations of b, c and s quarks using certain decays of these baryons. We propose to use the Standard Model ttbar and Wc samples to calibrate these measurements. We estimate that the Run 2 dataset will suffice for measuring the quark polarizations in these Standard Model samples with precisions of order 10%. We also propose various additional measurements for the near and far future that would help characterize the polarization transfer from the quarks to the baryons.Comment: 6 pages, proceedings of LFC17: Old and New Strong Interactions from LHC to Future Colliders, Trento, September 11-15, 201

    Searching for periodic signals in kinematic distributions using continuous wavelet transforms

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    Many models of physics beyond the Standard Model include towers of particles whose masses follow an approximately periodic pattern with little spacing between them. These resonances might be too weak to detect individually, but could be discovered as a group by looking for periodic signals in kinematic distributions. The continuous wavelet transform, which indicates how much a given frequency is present in a signal at a given time, is an ideal tool for this. In this paper, we present a series of methods through which continuous wavelet transforms can be used to discover periodic signals in kinematic distributions. Some of these methods are based on a simple test statistic, while others make use of machine learning techniques. Some of the methods are meant to be used with a particular model in mind, while others are model-independent. We find that continuous wavelet transforms can give bounds comparable to current searches and, in some cases, be sensitive to signals that would go undetected by standard experimental strategies.Comment: 22 pages, 7 figures, matches version published in EPJ
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