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dc.contributor.authorSirunyan, A. M
dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAmbrogi, F.
dc.contributor.authorBergauer, T.
dc.contributor.authorDragicevic, M.
dc.contributor.authorErö, J.
dc.contributor.authorValle, A. E D
dc.contributor.authorFlechl, M.
dc.contributor.authorFrühwirth, R.
dc.contributor.authorJeitler, M.
dc.contributor.authorKrammer, N.
dc.contributor.authorKrätschmer, I.
dc.contributor.authorLiko, D.
dc.date.accessioned2021-09-20T17:30:39Z
dc.date.available2021-09-20T17:30:39Z
dc.date.issued2020-10-30
dc.identifier.urihttps://hdl.handle.net/1721.1/131856
dc.description.abstractAbstract 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 $$\sqrt{s}=13\,\text {TeV} $$ s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $$\,\text {fb}^{-1}$$ 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 $$\hbox {b}\bar{\hbox {b}}$$ b b ¯ .en_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttps://doi.org/10.1007/s41781-020-00041-zen_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer International Publishingen_US
dc.titleA Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolutionen_US
dc.typeArticleen_US
dc.identifier.citationComputing and Software for Big Science. 2020 Oct 30;4(1):10en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-11-01T04:32:16Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2020-11-01T04:32:16Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


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