Abstract
Well known for its sensitivity to BSM physics, Anomalous triple-gauge boson couplings (aTGC) can be applied to the process of $e^{+}e^{-} \to W^{+}W^{-}$ to probe BSM physics. The Tradition methods, optimal observable, cannot provide precise limits on BSM parameters when considering the initial state radiation and detector effect because it can’t give an unbiased central value of the parameters. In this talk, we will introduce machine learning methods that meet the precision needs of aTGC measurement at a Higgs factory. The numerical result shows that the boundary based on the Machine Learning is stronger than the optimal observable, and the central value is much closer to the ideal case, demonstrating their potential to improve the precision of BSM physics measurement on future colliders.
