PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions
Abstract Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and ‘tag’ them to their emitter particle. Advances in jet tagging present opportunities for searches of new physics b...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-07-01
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| Series: | Journal of High Energy Physics |
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| Online Access: | https://doi.org/10.1007/JHEP07(2024)247 |
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| _version_ | 1850203624600240128 |
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| author | Yash Semlani Mihir Relan Krithik Ramesh |
| author_facet | Yash Semlani Mihir Relan Krithik Ramesh |
| author_sort | Yash Semlani |
| collection | DOAJ |
| description | Abstract Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and ‘tag’ them to their emitter particle. Advances in jet tagging present opportunities for searches of new physics beyond the Standard Model. Current approaches use deep learning to uncover hidden patterns in complex collision data. However, the representation of jets as inputs to a deep learning model have been varied, and often, informative features are withheld from models. In this study, we propose a graph-based representation of a jet that encodes the most information possible. To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has been demonstrated as an effective alternative to classical graph convolutions in GNNs and has yet to be explored in jet tagging. PCN achieves a substantial improvement in accuracy over existing taggers and opens the door to future studies into graph-based representations of jets and ChebConv layers in high-energy physics experiments. Code is available at https://github.com/YVSemlani/PCN-Jet-Tagging |
| format | Article |
| id | doaj-art-61a80f4efdc640d0a60f7af55ac39b2b |
| institution | OA Journals |
| issn | 1029-8479 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj-art-61a80f4efdc640d0a60f7af55ac39b2b2025-08-20T02:11:28ZengSpringerOpenJournal of High Energy Physics1029-84792024-07-012024711510.1007/JHEP07(2024)247PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutionsYash Semlani0Mihir Relan1Krithik Ramesh2University of North Carolina at Chapel HillJohns Hopkins UniversityMassachusetts Institute of TechnologyAbstract Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and ‘tag’ them to their emitter particle. Advances in jet tagging present opportunities for searches of new physics beyond the Standard Model. Current approaches use deep learning to uncover hidden patterns in complex collision data. However, the representation of jets as inputs to a deep learning model have been varied, and often, informative features are withheld from models. In this study, we propose a graph-based representation of a jet that encodes the most information possible. To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has been demonstrated as an effective alternative to classical graph convolutions in GNNs and has yet to be explored in jet tagging. PCN achieves a substantial improvement in accuracy over existing taggers and opens the door to future studies into graph-based representations of jets and ChebConv layers in high-energy physics experiments. Code is available at https://github.com/YVSemlani/PCN-Jet-Tagginghttps://doi.org/10.1007/JHEP07(2024)247Jets and Jet SubstructureTop Quark |
| spellingShingle | Yash Semlani Mihir Relan Krithik Ramesh PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions Journal of High Energy Physics Jets and Jet Substructure Top Quark |
| title | PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions |
| title_full | PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions |
| title_fullStr | PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions |
| title_full_unstemmed | PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions |
| title_short | PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions |
| title_sort | pcn a deep learning approach to jet tagging utilizing novel graph construction methods and chebyshev graph convolutions |
| topic | Jets and Jet Substructure Top Quark |
| url | https://doi.org/10.1007/JHEP07(2024)247 |
| work_keys_str_mv | AT yashsemlani pcnadeeplearningapproachtojettaggingutilizingnovelgraphconstructionmethodsandchebyshevgraphconvolutions AT mihirrelan pcnadeeplearningapproachtojettaggingutilizingnovelgraphconstructionmethodsandchebyshevgraphconvolutions AT krithikramesh pcnadeeplearningapproachtojettaggingutilizingnovelgraphconstructionmethodsandchebyshevgraphconvolutions |