Contributor info: Dr Alexander Mück

Gaetano Barone, Jiayi Chen, Stephane Cooperstein, Nikita Dolganov, Silvia Ferrario Ravasio, Yacine Haddad, Stefan Höche, Barbara Jäger, Alexander Karlberg, Alexander Mück, Mathieu Pellen, Christian T. Preuss, Daniel Reichelt, Simon Reinhardt, Marco Zaro

SciPost Phys. Comm. Rep. 13 (2025) · published 5 November 2025 | Toggle abstract · pdf

In this article, we summarise the recent experimental measurements and theoretical work on Higgs boson production via vector-boson fusion at the LHC. Along with this, we provide state-of-the-art predictions at fixed order as well as with parton-shower corrections within the Standard Model at $13.6\,\text{TeV}$. The results are presented in the form of multi-differential distributions as well as in the Simplified Template Cross Section bins. All materials and outputs of this study are available on public repositories. Finally, following findings in the literature, recommendations are made to estimate theoretical uncertainties related to parton-shower corrections.

Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn

SciPost Phys. 15, 168 (2023) · published 17 October 2023 | Toggle abstract · pdf

Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.

Elias Bernreuther, Thorben Finke, Felix Kahlhoefer, Michael Krämer, Alexander Mück

SciPost Phys. 10, 046 (2021) · published 23 February 2021 | Toggle abstract · pdf

Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons. Distinguishing such semi-visible jets from large QCD backgrounds is difficult and constitutes an exciting challenge for jet classification. In this article we explore the potential of supervised deep neural networks to identify semi-visible jets. We show that dynamic graph convolutional neural networks operating on so-called particle clouds outperform convolutional neural networks analysing jet images as well as other neural networks based on Lorentz vectors. We investigate how the performance depends on the properties of the dark shower and discuss training on mixed samples as a strategy to reduce model dependence. By modifying an existing mono-jet analysis we show that LHC sensitivity to dark sectors can be enhanced by more than an order of magnitude by using the dynamic graph network as a dark shower tagger.