20 citations · 38 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Scaling laws for amplitude surrogates
Henning Bahl, Victor Bresó-Pla, Anja Butter +1
Scaling laws describing the dependence of neural network performance on the amount of training data, the spent compute, and the network size have emerged across a huge variety of m…
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…