Detector-aware merged targets for calorimeter showers improve GNN particle flow reconstruction performance and robustness to topology changes on independent samples.
Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.
Randompack is a permissively licensed C library implementing several RNG engines and 14 distributions with cross-platform reproducibility and competitive or superior speed compared to existing libraries.
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.
A cascade pipeline on 400 AIE tiles evaluates gg→ttg leading-order matrix elements at 1 million per second with parts-per-million accuracy to MadGraph, delivering 34× CPU speedup and 7.7× better energy efficiency at 54.8 W.
A primer that surveys the architecture, methodologies, computational challenges, and future trajectory of the Monte Carlo event generator ecosystem in collider physics.
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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Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.
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Randompack: Cross-Platform Reproducible Random Number Generation and Distribution Sampling
Randompack is a permissively licensed C library implementing several RNG engines and 14 distributions with cross-platform reproducibility and competitive or superior speed compared to existing libraries.
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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
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FPGA Acceleration of Matrix-Element Calculations for Monte Carlo Event Generation
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.
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Cascade Pipeline for Leading-Order Matrix Element Evaluation on AMD Versal AI Engine Arrays
A cascade pipeline on 400 AIE tiles evaluates gg→ttg leading-order matrix elements at 1 million per second with parts-per-million accuracy to MadGraph, delivering 34× CPU speedup and 7.7× better energy efficiency at 54.8 W.
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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer that surveys the architecture, methodologies, computational challenges, and future trajectory of the Monte Carlo event generator ecosystem in collider physics.