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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.14759 |
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| _version_ | 1866908545487732736 |
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| author | Castillo, F. L. Levêque, J |
| author_facet | Castillo, F. L. Levêque, J |
| contents | Jet constituents provide a more detailed description of a jet's radiation pattern than global observables. In simulations for ATLAS Run-2 data (2015-2018), transformer-based taggers trained on low-level inputs outperformed traditional methods using high-level variables with conventional neural networks for quark-gluon discrimination. With the upcoming High-Luminosity LHC (HL-LHC), which will deliver higher luminosity and energy, the ATLAS detector will be upgraded with an extended Inner Tracker covering the forward region, previously uncovered by a tracking detector. This work studies how these upgrades will improve the accuracy and robustness of quark-gluon jet taggers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quark-Gluon tagging performance at the High-Luminosity LHC using constituent-based transformer models Castillo, F. L. Levêque, J High Energy Physics - Experiment Jet constituents provide a more detailed description of a jet's radiation pattern than global observables. In simulations for ATLAS Run-2 data (2015-2018), transformer-based taggers trained on low-level inputs outperformed traditional methods using high-level variables with conventional neural networks for quark-gluon discrimination. With the upcoming High-Luminosity LHC (HL-LHC), which will deliver higher luminosity and energy, the ATLAS detector will be upgraded with an extended Inner Tracker covering the forward region, previously uncovered by a tracking detector. This work studies how these upgrades will improve the accuracy and robustness of quark-gluon jet taggers. |
| title | Quark-Gluon tagging performance at the High-Luminosity LHC using constituent-based transformer models |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.14759 |