Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
Precision-machine learning for the matrix element method
The matrix element method is the LHC inference method of choice for limited statistics. We
present a dedicated machine learning framework, based on efficient phase-space …
present a dedicated machine learning framework, based on efficient phase-space …
Jet Diffusion versus JetGPT--Modern Networks for the LHC
[HTML][HTML] Reconstructing the kinematics of deep inelastic scattering with deep learning
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic
scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits …
scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits …
Measuring QCD splittings with invertible networks
QCD splittings are among the most fundamental theory concepts at the LHC. We show how
they can be studied systematically with the help of invertible neural networks. These …
they can be studied systematically with the help of invertible neural networks. These …