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Deconvolving Detector Effects for Distribution Moments
Krish Desai · Benjamin Nachman · Jesse Thaler

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in Machine Learning and the Physical Sciences »

Deconvolving (`unfolding') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most extant unfolding approaches require histogram binning while many theoretical predictions are at the level of moments. We develop a new approach to directly unfold distribution moments as a function of other observables without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics.

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Author Information

Krish Desai (University of California, Berkeley)
Benjamin Nachman (Lawrence Berkeley National Laboratory)
Jesse Thaler (MIT)

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