A 2HDM extended by two real scalar singlets is scanned with evolutionary strategies to locate regions satisfying vacuum, unitarity, oblique-parameter, collider and dark-matter constraints.
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Gradient boosted decision trees suppress diphoton backgrounds while adaptive symbolic memetic regression corrects beam deflection biases, reaching luminosity uncertainties below 10^{-4} and 5x10^{-6}.
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Machine Learning in the 2HDM2S model for Dark Matter
A 2HDM extended by two real scalar singlets is scanned with evolutionary strategies to locate regions satisfying vacuum, unitarity, oblique-parameter, collider and dark-matter constraints.
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Novel Machine Learning Methods to Improve Z Pole Integrated Luminosity at Future Colliders
Gradient boosted decision trees suppress diphoton backgrounds while adaptive symbolic memetic regression corrects beam deflection biases, reaching luminosity uncertainties below 10^{-4} and 5x10^{-6}.