Publications

Theo Heimel, Ramon Winterhalder, Anja Butter, Joshua Isaacson, Claudius Krause, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn (2023). MadNIS - Neural multi-channel importance sampling. SciPost Phys.15, 141.
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Simon Badger, Others (2023). Machine learning and LHC event generation. SciPost Phys.14, 079.
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Stefan Dittmaier, Philipp Maierhöfer, Christopher Schwan, Ramon Winterhalder (2023). Like-sign W-boson scattering at the LHC — approximations and full next-to-leading-order predictions. JHEP 2023, 22.
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Benjamin Nachman, Ramon Winterhalder (2023). Elsa: enhanced latent spaces for improved collider simulations. *Eur. Phys. J. C 83, 843 *.
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Tilman Plehn, Anja Butter, Barry Dillon, Theo Heimel, Claudius Krause, Ramon Winterhalder (2022). Modern Machine Learning for LHC Physicists.
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Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn (2022). Targeting multi-loop integrals with neural networks. SciPost Phys.12, 129.
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Miguel Arratia, Others (2022). Publishing unbinned differential cross section results. JINST 17 P01024.
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Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, David Shih, Ramon Winterhalder (2022). Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows. SciPost Phys.13, 087.
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Ramon Winterhalder, Marco Bellagente, Benjamin Nachman (2021). Latent Space Refinement for Deep Generative Models.
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Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder (2021). How to GAN Event Unweighting. SciPost Phys.10, 089.
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