12 papers
Generative Amplification with Surrogate Monte Carlo
Henning Bahl, Tilman Plehn, Rebecca Revelli
Amplitude surrogates for LHC simulations build on generative amplification, the fact that a surrogate trained on an expensive and small training dataset describes the smooth amplit…
Forecasting Generative Amplification
Henning Bahl, Sascha Diefenbacher, Nina Elmer +2
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is import…
Local Conformal Predictions for Calibrated Surrogates
Suprio Dubey, Henning Bahl, Anja Butter +2
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it using conform…
One Generator, Any Process: LLM-Conditioning for the LHC
Henning Bahl, Tilman Plehn, Daniel Schiller +1
Neural network training for LHC event generation should, ideally, benefit from common high-level patterns in different processes. We propose novel conditioning schemes for continuo…
How to Trust Learned Loop Amplitudes
Henning Bahl, Jens Braun, Gudrun Heinrich +2
Higher-order theory predictions are crucial for the precision LHC program, but the time-consuming amplitude evaluation challenges the corresponding Monte-Carlo simulations. Machine…
Sensitivity to new physics: single-Higgs couplings vs. the trilinear Higgs coupling
Henning Bahl, Johannes Braathen, Martin Gabelmann +4
The trilinear Higgs self-coupling provides a unique probe of the structure of the Higgs potential and of the nature of the electroweak phase transition, and constitutes a key targe…