20 citations · 38 across the 11 of their papers we have counts for
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…
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…
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…
Amplitude Uncertainties Everywhere All at Once
Henning Bahl, Nina Elmer, Tilman Plehn +1
Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles an…
-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…
ECFA Higgs, electroweak, and top Factory Study
H. Abidi, J. A. Aguilar-Saavedra, S. Airen +373
The ECFA Higgs, electroweak, and top Factory Study ran between 2021 and 2025 as a broad effort across the experimental and theoretical particle physics communities, bringing togeth…
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…
Advancing Tools for Simulation-Based Inference
Henning Bahl, Victor Bresó, Giovanni De Crescenzo +1
We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood est…