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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LHC vector boson fusion searches enhanced by machine learning can probe substantial regions of cosmologically viable parameter space for freeze-in dark matter mediated by a spin-2 particle.
Electroweak radiative corrections are essential for accurate collider predictions, with current techniques and progress illustrated through di-boson production, vector-boson scattering, and tri-boson production.
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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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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Probing Freeze-In Dark Matter via a Spin-2 Portal at the LHC with Vector Boson Fusion and Machine Learning
LHC vector boson fusion searches enhanced by machine learning can probe substantial regions of cosmologically viable parameter space for freeze-in dark matter mediated by a spin-2 particle.
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The status of theory in the electroweak sector: Radiative corrections, salient features, approximations
Electroweak radiative corrections are essential for accurate collider predictions, with current techniques and progress illustrated through di-boson production, vector-boson scattering, and tri-boson production.
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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.