Detector-aware merged targets for calorimeter showers improve GNN particle flow reconstruction performance and robustness to topology changes on independent samples.
Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
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Context-aware stress testing reveals that the local assumption fails for Z→ℓℓ reconstruction at HL-LHC, producing bias and degraded resolution that an unsupervised regime-mapping framework then corrects.
citing papers explorer
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Detector-aware target definitions for full-event particle reconstruction
Detector-aware merged targets for calorimeter showers improve GNN particle flow reconstruction performance and robustness to topology changes on independent samples.
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Stress testing of fast reconstruction pipelines using machine learning
Context-aware stress testing reveals that the local assumption fails for Z→ℓℓ reconstruction at HL-LHC, producing bias and degraded resolution that an unsupervised regime-mapping framework then corrects.