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@ARTICLE{Amacker:453758,
      author       = {Amacker, Jacob and Balunas, William and Beresford, Lydia
                      and Bortoletto, Daniela and Frost, James and Issever, Cigdem
                      and Liu, Jesse and McKee, James and Micheli, Alessandro and
                      Paredes Saenz, Santiago and Spannowsky, Michael and
                      Stanislaus, Beojan},
      title        = {{H}iggs self-coupling measurements using deep learning in
                      the $ b\overline{b}b\overline{b} $ final state},
      journal      = {Journal of high energy physics},
      volume       = {12},
      number       = {12},
      issn         = {1029-8479},
      address      = {[Trieste]},
      publisher    = {SISSA},
      reportid     = {PUBDB-2021-00149, arXiv:2004.04240. IPPP/20/11},
      pages        = {115},
      year         = {2020},
      abstract     = {Measuring the Higgs trilinear self-coupling λ$_{hhh}$ is
                      experimentally demanding but fundamental for understanding
                      the shape of the Higgs potential. We present a comprehensive
                      analysis strategy for the HL-LHC using di-Higgs events in
                      the four b-quark channel (hh → 4b), extending current
                      methods in several directions. We perform deep learning to
                      suppress the formidable multijet background with dedicated
                      optimisation for BSM λ$_{hhh}$ scenarios. We compare the
                      λ$_{hhh}$ constraining power of events using different
                      multiplicities of large radius jets with a two-prong
                      structure that reconstruct boosted h → bb decays. We show
                      that current uncertainties in the SM top Yukawa coupling
                      y$_{t}$ can modify λ$_{hhh}$ constraints by ∼ 20\%. For
                      SM y$_{t}$, we find prospects of −0.8 <$
                      {\lambda}_{hhh}/{\lambda}_{hhh}^{\mathrm{SM}} $< 6.6 at 68\%
                      CL under simplified assumptions for 3000 fb$^{−1}$ of
                      HL-LHC data. Our results provide a careful assessment of
                      di-Higgs identification and machine learning techniques for
                      all-hadronic measurements of the Higgs self-coupling and
                      sharpens the requirements for future improvement.},
      keywords     = {CERN LHC Coll: upgrade (INSPIRE) / potential: Higgs
                      (INSPIRE) / multiplicity: difference (INSPIRE) / jet:
                      multiple production (INSPIRE) / coupling: Yukawa (INSPIRE) /
                      structure (INSPIRE) / background (INSPIRE) / Higgs Physics
                      (autogen) / Beyond Standard Model (autogen)},
      cin          = {ZEU-EXP},
      ddc          = {530},
      cid          = {I:(DE-H253)ZEU-EXP-20120731},
      pnm          = {631 - Accelerator R $\&$ D (POF3-631)},
      pid          = {G:(DE-HGF)POF3-631},
      experiment   = {EXP:(DE-H253)LHC-Exp-ATLAS-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2004.04240},
      howpublished = {arXiv:2004.04240},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2004.04240;\%\%$},
      UT           = {WOS:000601400500001},
      doi          = {10.1007/JHEP12(2020)115},
      url          = {https://bib-pubdb1.desy.de/record/453758},
}