Classifying hadronic objects in ATLAS with ML/AI algorithms
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910050782543872 |
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| author | Toffolin, Leonardo |
| author_facet | Toffolin, Leonardo |
| contents | The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their origin, distinguishing between quark- and gluon-initiated jets, and identifying hadronically decaying heavy objects such as W bosons and top quarks. This contribution summarises recent developments in constituent-based tagging architectures, including graph neural networks (GNNs) and transformer-based approaches, their performance in simulated and real data, and future perspectives towards data-driven optimisation and model-independent tagging strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12306 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Classifying hadronic objects in ATLAS with ML/AI algorithms Toffolin, Leonardo Data Analysis, Statistics and Probability High Energy Physics - Experiment The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their origin, distinguishing between quark- and gluon-initiated jets, and identifying hadronically decaying heavy objects such as W bosons and top quarks. This contribution summarises recent developments in constituent-based tagging architectures, including graph neural networks (GNNs) and transformer-based approaches, their performance in simulated and real data, and future perspectives towards data-driven optimisation and model-independent tagging strategies. |
| title | Classifying hadronic objects in ATLAS with ML/AI algorithms |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2603.12306 |