Outputs (5)

Unweighting multijet event generation using factorisation-aware neural networks (2023)
Journal Article
Janßen, T., Maître, D., Schumann, S., Siegert, F., & Truong, H. (2023). Unweighting multijet event generation using factorisation-aware neural networks. SciPost Physics, 15(3), Article 107. https://doi.org/10.21468/scipostphys.15.3.107

In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on d... Read More about Unweighting multijet event generation using factorisation-aware neural networks.

Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples (2023)
Journal Article
Andersen, J. R., Maier, A., & Maître, D. (2023). Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples. The European Physical Journal C, 83(9), Article 835. https://doi.org/10.1140/epjc/s10052-023-11905-0

We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much l... Read More about Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples.

One-loop matrix element emulation with factorisation awareness (2023)
Journal Article
Maître, D., & Truong, H. (2023). One-loop matrix element emulation with factorisation awareness. Journal of High Energy Physics, 2023(5), Article 159. https://doi.org/10.1007/jhep05%282023%29159

In this article we present an emulation strategy for one-loop matrix elements. This strategy is based on the factorisation properties of matrix elements and is an extension of the work presented in [1]. We show that a percent-level accuracy can be ac... Read More about One-loop matrix element emulation with factorisation awareness.

Machine learning and LHC event generation (2023)
Journal Article
Butter, A., Plehn, T., Schumann, S., Badger, S., Caron, S., Cranmer, K., …Zupan, J. (2023). Machine learning and LHC event generation. SciPost Physics, 14(4), Article 079. https://doi.org/10.21468/scipostphys.14.4.079

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applic... Read More about Machine learning and LHC event generation.

Multi-variable integration with a neural network (2023)
Journal Article
Maître, D., & Santos-Mateos, R. (2023). Multi-variable integration with a neural network. Journal of High Energy Physics, 2023(3), https://doi.org/10.1007/jhep03%282023%29221

In this article we present a method for automatic integration of parametric integrals over the unit hypercube using a neural network. The method fits a neural network to the primitive of the integrand using a loss function designed to minimize the di... Read More about Multi-variable integration with a neural network.