| Home > Documents in process > Machine Learning Assisted Reconstruction of Hadron-Collider Events using Mini-Jets |
| Conference Presentation | PUBDB-2025-05550 |
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2025
Abstract: Reconstructing impactful physical observables from hadron collider data represents challenges due to combinatorial ambiguities and experimental effects. We propose a novel approach using mini-jets (R=0.1) as the sole reconstructed objects, employing a deep neural network for observable determination. This method condenses full event information into a manageable size, demonstrating superior efficiency and generality compared to classical algorithms for future LHC analyses.
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