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arXiv · 2210.03698

Reconstructing parton collisions with machine learning techniques

German F. R. Sborlini·David F. Rentería-Estrada·Roger J. Hernández-Pinto·Pia Zurita
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Abstract

Having access to the parton-level kinematics is important for understanding the internal dynamics of particle collisions. Here, we present new results aiming to an efficient reconstruction of parton collisions using machine-learning techniques. By simulating the collider events, we related experimentally-accessible quantities with the momentum fractions of the involved partons. We used photon-hadron production to exploit the cleanliness of the photon signal, including up to NLO QCD-QED corrections. Neural networks led to an outstanding reconstruction efficiency, suggesting a powerful strategy for unveiling the behaviour of the fundamental bricks of matter in high-energy collisions.

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German F. R. Sborlini, David F. Rentería-Estrada, Roger J. Hernández-Pinto, Pia Zurita. 2022-10-07. Reconstructing parton collisions with machine learning techniques. https://arxiv.org/abs/2210.03698

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