Reconstructing partonic kinematics at colliders with machine learning

URN to cite this document:: urn:nbn:de:bvb:355-epub-536743
DOI to cite this document:: 10.5283/epub.53674

Estrada, David F. Rentería ; Hernández-Pinto, Roger J. ; Sborlini, German F. R. ; Zurita, Pia

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 02 Feb 2023 10:03

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Abstract

In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. In proton-proton collisions, this represents a challenging task since extracting such information from experimental data is not straightforward. With this in mind, we propose to tackle ...