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arXiv · 2508.20541

Machine-learning based particle-flow algorithm in CMS

Farouk Mokhtar
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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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Farouk Mokhtar. 2025-08-28. Machine-learning based particle-flow algorithm in CMS. https://arxiv.org/abs/2508.20541

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