8 citations · 17 across the 10 of their papers we have counts for
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…
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…
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…
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…
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…
A Global View of the EDM Landscape
Skyler Degenkolb, Nina Elmer, Tanmoy Modak +2
Permanent electric dipole moments (EDMs) are sensitive probes of the symmetry structure of elementary particles, which in turn is closely tied to the baryon asymmetry in the univer…
Staying on Top of SMEFT-Likelihood Analyses
Nina Elmer, Maeve Madigan, Tilman Plehn +1
We present a new global SMEFT analysis of LHC data in the top sector. After updating our set of measurements, we show how public ATLAS likelihoods can be incorporated into an exter…