4 papers
Towards Precise Simulations and Inference for the Neutron EDM
Skyler Degenkolb, Luigi Favaro, Peter Fierlinger +3
Precision measurements of neutron properties, like its permanent electric dipole moment, rely on understanding complex experimental setups in detail. We show how the properties of…
-Analyses with Symbolic Regression
Henning Bahl, Elina Fuchs, Marco Menen +1
Searching for violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its…
Accurate Surrogate Amplitudes with Calibrated Uncertainties
Henning Bahl, Nina Elmer, Luigi Favaro +3
Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activa…
Profile Likelihoods on ML-Steroids
Theo Heimel, Tilman Plehn, Nikita Schmal
Profile likelihoods, for instance, describing global SMEFT analyses at the LHC are numerically expensive to construct and evaluate. Especially profiled likelihoods are notoriously…