Henning Bahl, Elina Fuchs, Marco Menen, Tilman Plehn
SciPost Phys. 20, 040 (2026) ·
published 11 February 2026
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Searching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially important if an unambiguous probe of a fundamental symmetry is required. We propose solving this problem by learning analytic formulas with symbolic regression. Using the complementary PySR and SymbolNet approaches, we learn $\mathcal{CP}$-sensitive observables at the detector level for WBF Higgs production and top-associated Higgs production. We find that they offer advantages in interpretability and performance.
Henning Bahl, Elina Fuchs, Marc Hannig, Marco Menen
SciPost Phys. Core 8, 006 (2025) ·
published 20 January 2025
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The Higgs-gluon interaction is crucial for LHC phenomenology. To improve the constraints on the $\mathcal{CP}$ structure of this coupling, we investigate Higgs production with two jets using machine learning. In particular, we exploit the $\mathcal{CP}$ sensitivity of the so far neglected phase space region that differs from the typical vector boson fusion-like kinematics. Our results indicate that further improvements in the current experimental limits could be achievable using our techniques. We also discuss the most relevant observables and how $\mathcal{CP}$ violation in the Higgs–gluon interaction can be disentangled from $\mathcal{CP}$ violation in the interaction between the Higgs boson and massive vector bosons. Assuming the absence of $\mathcal{CP}$-violating Higgs interactions with coloured beyond-the-Standard-Model states, our projected limits on a $\mathcal{CP}$-violating top-Yukawa coupling are competitive with more direct probes like top-associated Higgs production and limits from a global fit.
