Variational Autoencoders for Regression: Recovering Fully Leptonic bb̅W^+W^- in Di-Higgs Searches

The search for double Higgs production in bb̅W^+W^-, where both W bosons decay to leptons, has been rehabilitated as a good option to look for that key process to the Standard Model scalar sector study in the LHC. The missing neutrinos, however, hinder the reconstruction of useful information like the Higgs pair mass, which is very sensitive to the trilinear Higgs self-coupling. We present a solution to that problem using a Variational Autoencoder for Regression (VAER) to reconstruct the Higgs and top pairs decays hh,tt̅→ bb̅W^+W^-→ bb̅ℓ^+ℓ^' -ν_ℓν̅_ℓ^'. The algorithm predicts the invariant mass of non-resonant hh irrespective of the trilinear coupling, even for events whose Higgs self-couplings were never presented to it. VAER is also able to identify a new Higgs resonance in an unsupervised way, showing generalization power for events not presented in its training phase. Finally, we demonstrate that VAER prediction is as useful to statistical inference as ground truth simulated distributions by computing a χ^2 between trilinear coupling hypotheses based on binned invariant mass distributions of bb̅ℓ^+ℓ^' -ν_ℓν̅_ℓ^'.