https://doi.org/10.1140/epjc/s10052-023-11809-z
Regular Article - Theoretical Physics
Parton labeling without matching: unveiling emergent labelling capabilities in regression models
1
Department of Physics, University of California, Berkeley, 94720, Berkeley, CA, USA
2
Courant Institute of Mathematical Sciences, New York University, 10012, New York, NY, USA
3
Physics Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
4
Berkeley Institute for Data Science, University of California, 94720, Berkeley, CA, USA
Received:
29
April
2023
Accepted:
5
July
2023
Published online:
15
July
2023
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 
method.
© The Author(s) 2023
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