OmniFold: A Method to Simultaneously Unfold All Observables
This addresses the need for more efficient and comprehensive unfolding in particle physics, offering a new paradigm that is not incremental but transformative for data analysis in high-energy physics.
The paper tackles the problem of correcting collider data for detector effects by introducing OmniFold, an unbinned unfolding method that uses machine learning to reweight simulated datasets, enabling simultaneous measurement of all observables, including future ones, and demonstrating its application on a realistic jet substructure example from the Large Hadron Collider.
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize on all available information. Our approach is unbinned, works for arbitrarily high-dimensional data, and naturally incorporates information from the full phase space. We illustrate this technique on a realistic jet substructure example from the Large Hadron Collider and compare it to standard binned unfolding methods. This new paradigm enables the simultaneous measurement of all observables, including those not yet invented at the time of the analysis.