Preserving new physics while simultaneously unfolding all observables

Direct searches for new particles at colliders have traditionally been factorized into model proposals by theorists and testing experimentalists. With the recent advent of machine learning methods that allow simultaneous unfolding all observables in a given phase space region, there is opportunity to blur these traditional boundaries performing on unfolded data. This could facilitate research program where data are explored their natural high dimensionality with as little bias possible. We study how information about physics beyond Standard Model preserved full using an important target Large Hadron Collider (LHC): exotic Higgs boson decays involving hadronic final states. find if signal cross section enough, visible will show some cases, quantifiably encoded Finally, we still many cases when does not work fully or precisely, such small. serve benchmark enhancing LHC beyond.