Speaker
Description
We investigate the discovery potential for double Higgs production in a relatively overlooked $hh \to b \bar{b} W W^* \to b \bar{b} \ell^+ \ell^- + \;/\!\! \vec{P}_T$ final state. We supplement a novel kinematic method presented in Ref.[1] with jet images resulting from the $h\to b \bar b$ decay. Two $b$-quarks from the decay of the Higgs boson, are color-connected with each other. In contrast, two $b$-quarks in $t\bar t$ production (the major background) arise from the decays of top quarks, which are color-connected with initial states. Since the difference in color-flow will be reflected in the resulting hadron distributions, we utilize a Convolutional Neural Network (CNN) trained on jet images for the signal-to-background discrimination. We design a DNN architecture to successfully combine new kinematic variables and jet images. As a result, we can obtain a sizable improvement on the signal sensitivity. We discuss relative improvements at each stage and the correlations among different input variables. The proposed method can be easily generalized to the semi-leptonic channel. The $b \bar{b} W W^*$ channel would contribute to the combined analysis of double Higgs production along with the other final states.
[1] J. H. Kim, K.C. Kong, K. T. Matchev and M. Park, Probing the Triple Higgs Self-Interaction at the Large Hadron Collider, Phys.Rev.Lett. 122 (2019) no.9, 091801, [1807.11498].
