Simphony reproduces Geant4 detected-photon ratios, arrival times, and wavelengths at sub-percent level in a large LAr TPC benchmark while delivering 1053x optical transport speedup on RTX 4090.
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Madgraph5 aMC@NLO on GPUs and vector CPUs Experience with the first alpha release
Canonical reference. 75% of citing Pith papers cite this work as background.
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Pith papers citing it
Background
75% of classified citations
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background 7
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2026 7representative citing papers
A reweighting method creates model-agnostic likelihoods from histogram analyses, applied to Belle II B+ to K+ nu nubar data for WET constraints and light new physics searches.
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 review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
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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Accelerating Discovery: Model-Agnostic Likelihoods for the Reinterpretation of Particle Physics Results and their Application to the Belle II $B^{+}\to K^{+}\nu\bar{\nu}$ Measurement
A reweighting method creates model-agnostic likelihoods from histogram analyses, applied to Belle II B+ to K+ nu nubar data for WET constraints and light new physics searches.
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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.