VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment
A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3282 |
| _version_ | 1797615647416385536 |
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| author | Silvia Auricchio Francesco Cirotto Antonio Giannini |
| author_facet | Silvia Auricchio Francesco Cirotto Antonio Giannini |
| author_sort | Silvia Auricchio |
| collection | DOAJ |
| description | A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of the particle physics, the standard model, and from its extensions that foresee potential new physics phenomena. This physical process has a well-defined event topology in the final state and a distinctive detector signature. In this work, an ML approach based on the RNN architecture is developed to deal with hadronic-only event information in order to enhance the acceptance of this production mechanism in physics analysis of the data. This technique was applied to a physics analysis in the context of high-mass diboson resonance searches using data collected by the ATLAS experiment. |
| first_indexed | 2024-03-11T07:29:40Z |
| format | Article |
| id | doaj.art-5105c7a9c4164844ac670521a389f8e2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:29:40Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5105c7a9c4164844ac670521a389f8e22023-11-17T07:21:44ZengMDPI AGApplied Sciences2076-34172023-03-01135328210.3390/app13053282VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC ExperimentSilvia Auricchio0Francesco Cirotto1Antonio Giannini2Dipartimento di Fisica “Ettore Pancini”, Università Degli Studi di Napoli Federico II, Complesso Univ. Monte S. Angelo, Via Cinthia, 21-Edificio 6, 80126 Napoli, ItalyDipartimento di Fisica “Ettore Pancini”, Università Degli Studi di Napoli Federico II, Complesso Univ. Monte S. Angelo, Via Cinthia, 21-Edificio 6, 80126 Napoli, ItalyCenter of Innovation and Cooperation for Particle and Interaction (CICPI), University of Science and Technology of China (USTC), No. 96, JinZhai Road, Baohe District, Hefei 230026, ChinaA novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of the particle physics, the standard model, and from its extensions that foresee potential new physics phenomena. This physical process has a well-defined event topology in the final state and a distinctive detector signature. In this work, an ML approach based on the RNN architecture is developed to deal with hadronic-only event information in order to enhance the acceptance of this production mechanism in physics analysis of the data. This technique was applied to a physics analysis in the context of high-mass diboson resonance searches using data collected by the ATLAS experiment.https://www.mdpi.com/2076-3417/13/5/3282MLRNNphysicsATLASdiboson |
| spellingShingle | Silvia Auricchio Francesco Cirotto Antonio Giannini VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment Applied Sciences ML RNN physics ATLAS diboson |
| title | VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment |
| title_full | VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment |
| title_fullStr | VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment |
| title_full_unstemmed | VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment |
| title_short | VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment |
| title_sort | vbf event classification with recurrent neural networks at atlas s lhc experiment |
| topic | ML RNN physics ATLAS diboson |
| url | https://www.mdpi.com/2076-3417/13/5/3282 |
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