Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21007
 Title: A deep neural network for simultaneous estimation of b jet energy and resolution Authors: Collaboration, CMS Keywords: hep-ex;hep-ex Issue Date: 12-Dec-2019 Publisher: CMS Abstract: We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of $\sqrt{s}=$ 13 TeV at the CERN LHC. The algorithm is trained on a large simulated sample of b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb$^{-1}$. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to $\mathrm{b\bar{b}}$. URI: http://bura.brunel.ac.uk/handle/2438/21007 ISSN: http://arxiv.org/abs/1912.06046v1http://arxiv.org/abs/1912.06046v1 Other Identifiers: http://arxiv.org/abs/1912.06046v1http://arxiv.org/abs/1912.06046v1 Appears in Collections: Publications

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