11 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…
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…
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…
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…
FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD
Javier Mariño Villadamigo, Rikkert Frederix, Tilman Plehn +2
High-multiplicity events remain a bottleneck for LHC simulations due to their computational cost. We present a ML-surrogate approach to accelerate matrix element reweighting from l…