Contributor info: Dr Nina Elmer

Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder

SciPost Phys. Core 8, 073 (2025) · published 24 October 2025 | Toggle abstract · pdf

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 activation functions are systematically tested with Kolmogorov-Arnold Networks. Then, we train neural surrogates to simultaneously predict the target amplitude and an uncertainty for the prediction. We disentangle systematic uncertainties, learned by a well-defined likelihood loss, from statistical uncertainties, which require the introduction of Bayesian neural networks or repulsive ensembles. We test the coverage of the learned uncertainties using pull distributions to quantify the calibration of cutting-edge neural surrogates.

Nina Elmer, Maeve Madigan, Tilman Plehn, Nikita Schmal

SciPost Phys. 18, 108 (2025) · published 21 March 2025 | Toggle abstract · pdf

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 external global analysis and how our analysis benefits from the additional information. We find that, unlike for the Higgs and electroweak sector, the SMEFT analysis of the top sector is mostly limited by the theory uncertainties. Finally, we present the first global SFitter analysis combining the top and electroweak-Higgs sectors.

Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann, Tilman Plehn

SciPost Phys. 16, 035 (2024) · published 29 January 2024 | Toggle abstract · pdf

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.