Jul 6 – 8, 2021
Europe/Zurich timezone

Testing Universality in various Monte Carlo Generators in Deep Learning with Application in Higgs-boson pair searches in 2HDM

Jul 7, 2021, 11:40 AM


Mr Yi-Lun Chung (National Tsing Hua University (TW))


It is widely known that predictions for jet substructure features vary significantly between Monte Carlo generators. This is especially true for the output of deep neural networks (NN) trained with high-dimensional feature spaces to tag the origin of a jet. However, even though the spectra of a given NN varies between generators, it could be that the function learned by different generators is the same. We investigate the universality of jet substructure information by training a NN with a variety of generators and testing these NNs on the same generator. By fixing the testing generator, we can see if the NNs have learned to use the same information, even if the extent to which that information is expressed varied between training datasets. Our target physics process is boosted Higgs bosons and we explore the implications of universality on uncertainties for searches for new particles at the Large Hadron Collider and beyond.

Academic Rank PhD student
Affiliation National Tsing Hua University

Primary authors

Prof. Kingman Cheung (National Tsing Hua University (TW)) Mr Yi-Lun Chung (National Tsing Hua University (TW)) Prof. Shih-Chieh Hsu (University of Washington Seattle (US)) Dr Ben Nachman (Lawrence Berkeley National Lab. (US))

Presentation materials