Machine-learning based particle-flow algorithm in CMS

• URL: http://arxiv.org/abs/2508.20541v1
• Date: Thu, 28 Aug 2025 08:28:47 GMT
• Title: Machine-learning based particle-flow algorithm in CMS
• Authors: Farouk Mokhtar,
• Abstract summary: The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS.<n>One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass.
• Score: 0.03729553543096811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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