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Probing the Triple Higgs Self-Interaction at the Large Hadron Collider
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abstract
We propose a novel kinematic method to expedite the discovery of the double Higgs ($hh$) production in the $\ell^+\ell^- b \bar{b} + E_T \hspace{-0.52cm} \big / ~$ final state. We make full use of recently developed kinematic variables, as well as the variables $\it Topness$ for the dominant background (top quark pair production) and $\it Higgsness$ for the signal. We obtain a significant increase in sensitivity compared to the previous analyses which used sophisticated algorithms like boosted decision trees or neutral networks. The method can be easily generalized to resonant $hh$ production as well as other non-resonant channels.
Forward citations
Cited by 2 Pith papers
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Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC
An attention-based full-event classifier constrains the Higgs self-coupling to (-0.53, 6.01) at 68% CL in the HH to 4b channel, a projected improvement over cut-based analyses.
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Improved analysis of non-resonant Higgs boson pair production in the $b\bar{b}\tau^+\tau^-$ final state with $196$ fb$^{-1}$ of data collected at $\sqrt{s}$ = 13 TeV and 13.6 TeV with the ATLAS detector
ATLAS finds μ_HH = 2.6^{+1.4}_{-1.0} in bbττ with Run 2+3 data, 2.6σ over background-only, and first 3.5σ evidence for ZH in this final state.
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