期刊
PHYSICAL REVIEW LETTERS
卷 124, 期 18, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.124.182001
关键词
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资金
- National Science Foundation [PHY-1607611]
- U.S. Department of Energy (DOE), Office of Science [DE-AC02-05CH11231]
- DOE Office of Nuclear Physics [DE-SC0011090]
- DOEOffice of High Energy Physics [DE-SC0012567, DE-SC0019128]
- Simons Foundation through a Simons Fellowship in Theoretical Physics
- U.S. Department of Energy (DOE) [DE-SC0019128] Funding Source: U.S. Department of Energy (DOE)
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.
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