Reconstructing partonic kinematics at colliders with machine learning
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...
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| Format: | Article |
| Language: | English |
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SciPost
2022-10-01
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| Series: | SciPost Physics Core |
| Online Access: | https://scipost.org/SciPostPhysCore.5.4.049 |
| _version_ | 1811246721599012864 |
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| author | David F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia Zurita |
| author_facet | David F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia Zurita |
| author_sort | David F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia Zurita |
| collection | DOAJ |
| description | 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 this problem by studying the production of one hadron and a direct photon in proton-proton collisions, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision. |
| first_indexed | 2024-04-12T14:57:30Z |
| format | Article |
| id | doaj.art-e328d36284da4887b194322c18f737ea |
| institution | Directory Open Access Journal |
| issn | 2666-9366 |
| language | English |
| last_indexed | 2024-04-12T14:57:30Z |
| publishDate | 2022-10-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics Core |
| spelling | doaj.art-e328d36284da4887b194322c18f737ea2022-12-22T03:28:12ZengSciPostSciPost Physics Core2666-93662022-10-015404910.21468/SciPostPhysCore.5.4.049Reconstructing partonic kinematics at colliders with machine learningDavid F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia ZuritaIn 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 this problem by studying the production of one hadron and a direct photon in proton-proton collisions, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision.https://scipost.org/SciPostPhysCore.5.4.049 |
| spellingShingle | David F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia Zurita Reconstructing partonic kinematics at colliders with machine learning SciPost Physics Core |
| title | Reconstructing partonic kinematics at colliders with machine learning |
| title_full | Reconstructing partonic kinematics at colliders with machine learning |
| title_fullStr | Reconstructing partonic kinematics at colliders with machine learning |
| title_full_unstemmed | Reconstructing partonic kinematics at colliders with machine learning |
| title_short | Reconstructing partonic kinematics at colliders with machine learning |
| title_sort | reconstructing partonic kinematics at colliders with machine learning |
| url | https://scipost.org/SciPostPhysCore.5.4.049 |
| work_keys_str_mv | AT davidfrenteriaestradarogerjhernandezpintogermanfrsborlinipiazurita reconstructingpartonickinematicsatcolliderswithmachinelearning |