The shape of the Higgs potential is modified by the presence of additional scalar fields, as predicted in many Beyond-Standard-Model (BSM) scenarios. In such cases, deviations in the Higgs self-interactions — in particular the trilinear Higgs couplings — could serve to disentangle the physics beyond the Standard Model (SM). While the SM predicts only one trilinear Higgs coupling, extended scalar sectors allow for additional self-interactions that can manifest themselves in Higgs pair production, via the s-channel contribution of a heavy 𝒞𝒫-even scalar H. We present the first sensitivity study to such a BSM trilinear scalar coupling using machine learning. Specifically, we train a neural network on the invariant mass distributions of Higgs pair production at the HL-LHC to extract <InlineEquation ID="IEq1"> <EquationSource Format="MATHML"><math display="inline"> <msubsup> <mi>ξ</mi> <mi>H</mi> <mi>t</mi> </msubsup> <mo>×</mo> <msub> <mi>λ</mi> <mi mathvariant="italic">hhH</mi> </msub> </math></EquationSource> <EquationSource Format="TEX">$ {\xi}_H^t\times {\lambda}_{hhH} $</EquationSource> </InlineEquation>, i.e. the product of the resonant H top-Yukawa coupling and the trilinear coupling of H to the two SM-like Higgses in the final state, hh. Assuming a hypothetical H mass of 450 GeV, we show that, depending on future experimental efficiencies and uncertainties, a determination of <InlineEquation ID="IEq2"> <EquationSource Format="MATHML"><math display="inline"> <msubsup> <mi>ξ</mi> <mi>H</mi> <mi>t</mi> </msubsup> <mo>×</mo> <msub> <mi>λ</mi> <mi mathvariant="italic">hhH</mi> </msub> </math></EquationSource> <EquationSource Format="TEX">$ {\xi}_H^t\times {\lambda}_{hhH} $</EquationSource> </InlineEquation> at the 10–20% level may be achievable by the end of the HL-LHC. We present a simple and more efficient alternative to classical statistical methods, proving the efficiency of neural networks for both hypothesis testing and parameter estimation, which outperforms conventional maximum likelihood methods in this context.

Experimental determination of BSM triple Higgs couplings at the HL-LHC with neural networks

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2026年6月26日

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Markus Frank,
Sven Heinemeyer,
Margarete Mühlleitner,
Kateryna Radchenko

摘要

The shape of the Higgs potential is modified by the presence of additional scalar fields, as predicted in many Beyond-Standard-Model (BSM) scenarios. In such cases, deviations in the Higgs self-interactions — in particular the trilinear Higgs couplings — could serve to disentangle the physics beyond the Standard Model (SM). While the SM predicts only one trilinear Higgs coupling, extended scalar sectors allow for additional self-interactions that can manifest themselves in Higgs pair production, via the s-channel contribution of a heavy 𝒞𝒫-even scalar H. We present the first sensitivity study to such a BSM trilinear scalar coupling using machine learning. Specifically, we train a neural network on the invariant mass distributions of Higgs pair production at the HL-LHC to extract $ξ_{H}^{t} \times λ_{hhH}$ $ {\xi}_H^t\times {\lambda}_{hhH} $ , i.e. the product of the resonant H top-Yukawa coupling and the trilinear coupling of H to the two SM-like Higgses in the final state, hh. Assuming a hypothetical H mass of 450 GeV, we show that, depending on future experimental efficiencies and uncertainties, a determination of $ξ_{H}^{t} \times λ_{hhH}$ $ {\xi}_H^t\times {\lambda}_{hhH} $ at the 10–20% level may be achievable by the end of the HL-LHC. We present a simple and more efficient alternative to classical statistical methods, proving the efficiency of neural networks for both hypothesis testing and parameter estimation, which outperforms conventional maximum likelihood methods in this context.