Machine-learned particle flow as a foundation model for collider physics
The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks. We show that casting event reconstruction as a machine learning problem naturally produces such a shared representation. We repurpose a machine learning model trained for particle-flow reconstruction (MLPF) to perform three distinct analysis tasks: jet flavor identification, jet energy regression, and missing momentum regression. By appending the per-particle latent representations learned during reconstruction as additional input features, we sub...
| Authors | Farouk Mokhtar, Joosep Pata, Michael Kagan et al. |
|---|---|
| Primary category | hep-ex |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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