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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20541 |
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| _version_ | 1866909757574479872 |
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| author | Mokhtar, Farouk |
| author_facet | Mokhtar, Farouk |
| contents | The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We present recent CMS developments in MLPF, including training datasets, model architecture, reconstruction metrics, and integration with offline reconstruction software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-learning based particle-flow algorithm in CMS Mokhtar, Farouk High Energy Physics - Experiment Machine Learning The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We present recent CMS developments in MLPF, including training datasets, model architecture, reconstruction metrics, and integration with offline reconstruction software. |
| title | Machine-learning based particle-flow algorithm in CMS |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2508.20541 |