Parton labeling without matching: unveiling emergent labelling capabilities in regression models

Qiu, Shikai; Han, Shuo; Ju, Xiangyang; Nachman, Benjamin; Wang, Haichen

doi:10.1140/epjc/s10052-023-11809-z

Title: Parton labeling without matching: unveiling emergent labelling capabilities in regression models

Journal Article · 14 July 2023 · European Physical Journal. C, Particles and Fields (Online)

DOI: https://doi.org/10.1140/epjc/s10052-023-11809-z · OSTI ID:2007216

Qiu, Shikai ^[1]; Han, Shuo ^[2]; Ju, Xiangyang ^[2]; Nachman, Benjamin ^[3]; Wang, Haichen ^[3]

University of California, Berkeley, CA (United States); New York University, NY (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely used $$χ$$² method.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2007216

Journal Information:: European Physical Journal. C, Particles and Fields (Online), Journal Name: European Physical Journal. C, Particles and Fields (Online) Journal Issue: 7 Vol. 83; ISSN 1434-6052

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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