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Direct searches for new particles at colliders have traditionally beenfactorized into model proposals by theorists and model testing byexperimentalists. With the recent advent of machine learning methods that allowfor the simultaneous unfolding of all observables in a given phase spaceregion, there is a new opportunity to blur these traditional boundaries byperforming searches on unfolded data. This could facilitate a research programwhere data are explored in their natural high dimensionality with as littlemodel bias as possible. We study how the information about physics beyond theStandard Model is preserved by full phase space unfolding using an importantphysics target at the Large Hadron Collider (LHC): exotic Higgs boson decaysinvolving hadronic final states. We find that if the signal cross section ishigh enough, information about the new physics is visible in the unfolded data.We will show that in some cases, quantifiably all of the information about thenew physics is encoded in the unfolded data. Finally, we show that there arestill many cases when the unfolding does not work fully or precisely, such aswhen the signal cross section is small. This study will serve as an importantbenchmark for enhancing unfolding methods for the LHC and beyond.

Preserving New Physics while Simultaneously Unfolding All Observables