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@ARTICLE{Hayrapetyan:638318,
      author       = {Hayrapetyan, Aram and others},
      collaboration = {{CMS Collaboration}},
      title        = {{D}eep{MET}: {I}mproving missing transverse momentum
                      estimation with a deep neural network},
      reportid     = {PUBDB-2025-04036, arXiv:2509.12012. CMS-JME-24-001.
                      CERN-EP-2025-193},
      year         = {2025},
      note         = {Submitted to Physical Review D. All figures and tables can
                      be found at
                      http://cms-results.web.cern.ch/cms-results/public-results/publications/JME-24-001
                      (CMS Public Pages)},
      abstract     = {At hadron colliders, the net transverse momentum of
                      particles that do not interact with the detector (missing
                      transverse momentum, $\vec{p}_\mathrm{T}^\text{miss}$) is a
                      crucial observable in many analyses. In the standard model,
                      $\vec{p}_\mathrm{T}^\text{miss}$ originates from neutrinos.
                      Many beyond-the-standard-model particles, such as dark
                      matter candidates, are also expected to leave the
                      experimental apparatus undetected. This paper presents a
                      novel $\vec{p}_\mathrm{T}^\text{miss}$ estimator, DeepMET,
                      which is based on deep neural networks that were developed
                      by the CMS Collaboration at the LHC. The DeepMET algorithm
                      produces a weight for each reconstructed particle based on
                      its properties. The estimator is based on the negative
                      vector sum of the weighted transverse momenta of all
                      reconstructed particles in an event. Compared with other
                      estimators currently employed by CMS, DeepMET improves the
                      $\vec{p}_\mathrm{T}^\text{miss}$ resolution by 10$-$30\%,
                      shows improvement for a wide range of final states, is
                      easier to train, and is more resilient against the effects
                      of additional proton-proton interactions accompanying the
                      collision of interest.},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) /
                      HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$ / DFG project G:(GEPRIS)390833306 -
                      EXC 2121: Quantum Universe (390833306)},
      pid          = {G:(DE-HGF)POF4-611 / $G:(DE-HGF)2019_IVF-HIDSS-0002$ /
                      G:(GEPRIS)390833306},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2509.12012},
      howpublished = {arXiv:2509.12012},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2509.12012;\%\%$},
      url          = {https://bib-pubdb1.desy.de/record/638318},
}