2022
DOI: 10.1007/jhep11(2022)045
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Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes

Abstract: Revealing the Higgs pair production process is the next big challenge in high energy physics. In this work, we explore the use of interpretable machine learning and cooperative game theory for extraction of the trilinear Higgs self-coupling in Higgs pair production. In particular, we show how a topological decomposition of the gluon-gluon fusion Higgs pair production process can be used to simplify the machine learning analysis flow. Furthermore, we extend the analysis to include $$ q\overline{q} $$ … Show more

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Cited by 10 publications
(2 citation statements)
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“…The Shapley value after considering all possible subsets T not containing i, i.e. the payoff of the i-th player, is defined as [115,116]…”
Section: Jhep05(2024)232mentioning
confidence: 99%
“…The Shapley value after considering all possible subsets T not containing i, i.e. the payoff of the i-th player, is defined as [115,116]…”
Section: Jhep05(2024)232mentioning
confidence: 99%
“…For example, one can measure the light quark Yukawa coupling through i) rare modes of Higgs boson decays, e.g. h → J/Ψγ (ϕγ, ργ, ωγ) [4][5][6]; ii) Higgs production in association with a charmtagged jet [7]; iii) global analysis of the Higgs data [8][9][10][11]; iv) the transverse momentum (p T ) distribution of Higgs boson or jet in Higgs production processes [12][13][14][15][16][17]; v) the kinematic shapes of Higgs pair production [18], off-shell Higgs production [19,20] and triple heavy vector boson production [21]; etc. The above proposals demand accurate calculations of light quark meson formation, charm tagging efficiency and faking rates of light quarks, or precise knowledge of the p T spectrum of Higgs boson.…”
Section: Introductionmentioning
confidence: 99%