  Neutrinos, dark matter, and long-lived neutral particles traverse the
particle detectors unnoticed, carrying away information about their parent
particles and interaction sources needed to reconstruct key variables like
resonance peaks in invariant mass distributions. In this work, we show that a
$k$-nearest neighbors regressor algorithm combined with deep neural network
classifiers, a $k$NN, is able to accurately recover binned distributions of the
fully leptonic $WW$ mass of a new heavy Higgs boson and its Standard Model
backgrounds from the observable detector level information at disposal. The
output of the regressor can be used to train even stronger classifiers to
separate signals and backgrounds in the fully leptonic case and guarantee the
selection of on-mass-shell Higgs bosons with enhanced statistical significance.
The method assumes previous knowledge of the event classes and model
parameters, thus suitable for post-discovery studies.
