MadNIS -- Neural Multi-Channel Importance Sampling
(2023) SciPost Physics — Vol. 15 (2023)
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- Authors
Winterhalder, Ramon 
- et. al.
- Abstract
- Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.
- Affiliations
Citations
Winterhalder, R., Maltoni, F., Mattelaer, O., & et al. (2023). MadNIS -- Neural Multi-Channel Importance Sampling. SciPost Physics, 15. https://doi.org/10.21468/SciPostPhys.15.4.141 (Original work published 2023)