Reconstruction of Missing Resonances Combining Nearest Neighbors Regressors and Neural Network Classifiers

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.