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Found 5 papers, 0 papers with code
The Landscape of Unfolding with Machine Learning
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
We introduce the iterative conditional INN~(IcINN) for unfolding that adjusts for deviations between simulated training samples and data.
Generative Networks for Precision Enthusiasts
Generative networks are opening new avenues in fast event generation for the LHC.
How to GAN Higher Jet Resolution
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution
High Energy Physics - Phenomenology
GANplifying Event Samples
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.
