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2021

Preserving New Physics while Simultaneously Unfolding All Observables

Komiske, Patrick, McCormack, W. Patrick, Nachman, Benjamin

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Direct searches for new particles at colliders have traditionally been factorized into model proposals by theorists and model testing by experimentalists.

With the recent advent of machine learning methods that allow for the simultaneous unfolding of all observables in a given phase space region, there is a new opportunity to blur these traditional boundaries by performing searches on unfolded data.
This could facilitate a research program where data are explored in their natural high dimensionality with as little model bias as possible.
We study how the information about physics beyond the Standard Model is preserved by full phase space unfolding using an important physics target at the Large Hadron Collider (LHC): exotic Higgs boson decays involving hadronic final states.

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