Symbolic regression produces analytic, detector-level CP-odd observables for WBF Higgs production and an analytic reconstruction of the Collins-Soper angle in ttH that are competitive with black-box ML and classical methods.
Machine-enhanced CP-asymmetries in the Higgs sector
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abstract
Improving the sensitivity to CP-violation in the Higgs sector is one of the pillars of the precision Higgs programme at the Large Hadron Collider. We present a simple method that allows CP-sensitive observables to be directly constructed from the output of neural networks. We show that these observables have improved sensitivity to CP-violating effects in the production and decay of the Higgs boson, when compared to the use of traditional angular observables alone. The kinematic correlations identified by the neural networks can be used to design new analyses based on angular observables, with a similar improvement in sensitivity.
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$\mathcal{CP}$-Analyses with Symbolic Regression
Symbolic regression produces analytic, detector-level CP-odd observables for WBF Higgs production and an analytic reconstruction of the Collins-Soper angle in ttH that are competitive with black-box ML and classical methods.