SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
org/10.1007/s41781-018-0018-8
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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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SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
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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.