Publications by authors named "J Rander"

Article Synopsis
  • The study focuses on detecting multijet signatures from proton-proton collisions at a high energy of 13 TeV, analyzing a dataset totaling 128 fb^{-1}.
  • A special data scouting method is utilized to pick out events with low combined momentum in jets.
  • This research is pioneering in its investigation of electroweak particle production in R-parity violating supersymmetric models, particularly examining hadronically decaying mass-degenerate higgsinos, and it broadens the limits on the existence of R-parity violating top squarks and gluinos.
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The first search for the Z boson decay to ττμμ at the CERN LHC is presented, based on data collected by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138  fb^{-1}. The data are compatible with the predicted background. For the first time, an upper limit at the 95% confidence level of 6.

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Observation of the decay.

Eur Phys J C Part Fields

October 2024

Using proton-proton collision data corresponding to an integrated luminosity of collected by the CMS experiment at , the decay is observed for the first time, with a statistical significance exceeding 5 standard deviations. The relative branching fraction, with respect to the decay, is measured to be , where the first uncertainty is statistical, the second is systematic, and the third is related to the uncertainties in and .

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A search for collective effects inside jets produced in proton-proton collisions is performed via correlation measurements of charged particles using the CMS detector at the CERN LHC. The analysis uses data collected at a center-of-mass energy of sqrt[s]=13  TeV, corresponding to an integrated luminosity of 138  fb^{-1}. Jets are reconstructed with the anti-k_{T} algorithm with a distance parameter of 0.

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Article Synopsis
  • Demand for computing power in major scientific experiments, like the CMS at CERN, is expected to significantly increase over the coming decades.
  • The implementation of coprocessors, particularly GPUs, in data processing workflows can enhance performance and efficiency, especially for machine learning tasks.
  • The Services for Optimized Network Inference on Coprocessors (SONIC) approach allows for improved use of coprocessors, demonstrating successful integration and acceleration of workflows across various environments without sacrificing throughput.
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