REVIEW 3 cited by
Neural Network-Based Approach to Phase Space Integration
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
read the original abstract
Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces. We present a Neural Network (NN) algorithm optimized to perform this task. The algorithm has been applied to several examples of direct relevance for particle physics, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements. Excellent performance has been demonstrated in all examples, with the properly trained NN achieving unweighting efficiencies of between 30% and 75%. In contrast to traditional Monte Carlo algorithms such as VEGAS, the NN-based approach does not require that the phase space coordinates be aligned with resonant or other features in the cross section.
Forward citations
Cited by 3 Pith papers
-
Schr\"{o}dinger Generator for High-Dimensional Integration and Sampling on Quantum Many-Body States
A two-stage sampler (adaptive marginal map plus normalizing flow, then resampling) is proposed and shown on model nuclear densities up to D=624, though a core Jacobian equation appears sign-inconsistent.
-
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.
-
LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning
A neural network learns isocontour-defined partition regions of an integrand, providing cheap region classification and volume estimates for stratified Monte Carlo integration and event unweighting.
Discussion (0). Continue with ORCID to comment.