OmniFold: A Method to Simultaneously Unfold All Observables

被引：82

作者：

Andreassen, Anders ^{[1
,2
,3
]}

Komiske, Patrick T. ^{[4
]}

Metodiev, Eric M. ^{[4
]}

Nachman, Benjamin ^{[2
]}

Thaler, Jesse ^{[4
]}

机构：

[1] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA

[2] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA 94720 USA

[3] Google, Mountain View, CA 94043 USA

[4] MIT, Ctr Theoret Phys, Cambridge, MA 02139 USA

来源：

PHYSICAL REVIEW LETTERS | 2020年 / 124卷 / 18期

基金：

美国国家科学基金会;

关键词：

Iterative methods;

D O I：

10.1103/PhysRevLett.124.182001

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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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