Prompt and non-prompt production of charm hadrons in proton-proton collisions at the Large Hadron Collider using machine learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912325020155904 |
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| author | Sahoo, Raghunath Prasad, Suraj Mallick, Neelkamal Goswami, Kangkan Mohanty, Gagan B. |
| author_facet | Sahoo, Raghunath Prasad, Suraj Mallick, Neelkamal Goswami, Kangkan Mohanty, Gagan B. |
| contents | In this contribution, we use machine learning (ML) based models to separate the prompt and non-prompt production of heavy flavour hadrons, such as $D^0$ and J/$ψ$, in proton-proton collisions at LHC energies. For this purpose, we use PYTHIA~8 to generate events, which provides a good qualitative agreement with experimental measurements of charm hadron production. The input features for the ML models are experimentally measurable. The prediction accuracy of the ML models used in this study reaches up to 99\%. The ML models can be useful in providing precise track-level identification, which is not possible in experiments with traditional methods. The contribution also discusses future applications of the ML models to understand the production of prompt and non-prompt heavy quark hadrons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt and non-prompt production of charm hadrons in proton-proton collisions at the Large Hadron Collider using machine learning Sahoo, Raghunath Prasad, Suraj Mallick, Neelkamal Goswami, Kangkan Mohanty, Gagan B. High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory Nuclear Experiment Nuclear Theory In this contribution, we use machine learning (ML) based models to separate the prompt and non-prompt production of heavy flavour hadrons, such as $D^0$ and J/$ψ$, in proton-proton collisions at LHC energies. For this purpose, we use PYTHIA~8 to generate events, which provides a good qualitative agreement with experimental measurements of charm hadron production. The input features for the ML models are experimentally measurable. The prediction accuracy of the ML models used in this study reaches up to 99\%. The ML models can be useful in providing precise track-level identification, which is not possible in experiments with traditional methods. The contribution also discusses future applications of the ML models to understand the production of prompt and non-prompt heavy quark hadrons. |
| title | Prompt and non-prompt production of charm hadrons in proton-proton collisions at the Large Hadron Collider using machine learning |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2504.09541 |