Optimizing Observables with Machine Learning for Better Unfolding

Arratia, Miguel; Britzger, Daniel; Long, Owen; Nachman, Benjamin

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Optimizing Observables with Machine Learning for Better Unfolding

Arratia, M., Britzger, D., Long, O., & Nachman, B. (2022). Optimizing Observables with Machine Learning for Better Unfolding. Journal of Instrumentation, 17, P07009. Retrieved from https://publications.mppmu.mpg.de/?action=search&mpi=MPP-2022-35.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-B4EB-0 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-B4EC-F

Genre: Journal Article

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Creators:
Arratia, Miguel¹, Author
Britzger, Daniel¹, Author
Long, Owen¹, Author
Nachman, Benjamin¹, Author

Affiliations:
1Max Planck Institute for Physics, Max Planck Society and Cooperation Partners, ou_2253650

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Free keywords: Phenomenology of High Energy Physics

Abstract: Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.

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Dates: Date issued: 2022

Publication Status: Issued

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Identifiers: Other: MPP-2022-35
URI: https://publications.mppmu.mpg.de/?action=search&mpi=MPP-2022-35
arXiv: arxiv:2203.16722
URI: https://inspirehep.net/literature/2060736

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Title: Journal of Instrumentation

Abbreviation : J.Inst.

Source Genre: Journal

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Pages: - Volume / Issue: 17 Sequence Number: - Start / End Page: P07009 Identifier: -