Compact Muon Solenoid
LHC, CERN
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| CMS-PAS-HIG-18-027 | ||
| A deep neural network for simultaneous estimation of b quark energy and resolution | ||
| CMS Collaboration | ||
| September 2019 | ||
| Abstract: We describe a method to obtain point and dispersion estimates for the energy of jets arising from bottom quarks (b jets) in proton-proton (pp) collisions at the CERN LHC. The algorithm is trained using a large simulated sample of b jets produced in pp collisions recorded at an energy of $\sqrt{s}= $ 13 TeV and validated on data recorded by the CMS detector in 2017 with an integrated luminosity of 41 fb$^{-1}$. A multivariate regression estimator employing jet composition and structure information and the properties of the associated reconstructed secondary vertices is implemented using a deep feed-forward neural network. The results of the algorithm are used to improve the experimental sensitivity of analyses that make use of b jets in the final state, such as the recently published observation of the Higgs boson decay to a bottom quark-antiquark pair. | ||
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Links:
CDS record (PDF) ;
inSPIRE record ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, CSBS 4 (2020) 10. The superseded preliminary plots can be found here. |
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Compact Muon Solenoid LHC, CERN |
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