REVIEW 4 cited by
Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library and interfaced to the Sherpa Monte Carlo event generator where we perform a detailed study for $2\to3$ and $2\to4$ scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.
Forward citations
Cited by 4 Pith papers
-
MadSpace -- Event Generation for the Era of GPUs and ML
MadSpace is a GPU-native compute-graph event-generation library that matches MadGraph LO physics in validation and introduces the analytic FastRambo phase-space mapping.
-
A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda}_{3H}$
A new POWHEG–MoMEMta interface and 'Block N' phase-space block realize the first MEM@NLO for gg→HH→bbγγ, recovering κλ=1 within ~0.5 expected uncertainty on Monte Carlo pseudo-experiments.
-
FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD
An ML surrogate for the leading-to-full-color reweighting factor accelerates QCD event generation by up to a factor of two while preserving full-color accuracy.
-
Explainable AI-assisted Optimization for Feynman Integral Reduction
FunSearch discovered a simple priority function for ordering IBP seeding integrals, reducing the number needed for multi-loop Feynman integral reductions by factors up to 3058.
Discussion (0). Continue with ORCID to comment.