| The machine learning landscape of top taggers G Kasieczka, T Plehn, A Butter, K Cranmer, D Debnath, BM Dillon, ... SciPost Physics 7 (1), 014, 2019 | 288 | 2019 |
| The gauge-Higgs legacy of the LHC Run I A Butter, OJP Éboli, J Gonzalez-Fraile, MC Gonzalez-Garcia, T Plehn, ... Journal of High Energy Physics 2016 (7), 1-21, 2016 | 210 | 2016 |
| Deep-learned top tagging with a Lorentz layer A Butter, G Kasieczka, T Plehn, M Russell SciPost Physics 5 (3), 028, 2018 | 185 | 2018 |
| How to GAN LHC events A Butter, T Plehn, R Winterhalder SciPost Physics 7 (6), 075, 2019 | 152 | 2019 |
| Invertible networks or partons to detector and back again M Bellagente, A Butter, G Kasieczka, T Plehn, A Rousselot, ... SciPost Physics 9 (5), 074, 2020 | 116 | 2020 |
| Event generators for high-energy physics experiments JM Campbell, M Diefenthaler, TJ Hobbs, S Höche, J Isaacson, F Kling, ... arXiv preprint arXiv:2203.11110, 2022 | 115 | 2022 |
| How to GAN away detector effects M Bellagente, A Butter, G Kasieczka, T Plehn, R Winterhalder SciPost Physics 8 (4), 070, 2020 | 115 | 2020 |
| Fox-Wolfram moments in Higgs physics C Bernaciak, MSA Buschmann, A Butter, T Plehn Physical Review D—Particles, Fields, Gravitation, and Cosmology 87 (7), 073014, 2013 | 102 | 2013 |
| Machine learning and LHC event generation A Butter, T Plehn, S Schumann, S Badger, S Caron, K Cranmer, ... SciPost physics 14 (4), 079, 2023 | 94 | 2023 |
| GANplifying event samples A Butter, S Diefenbacher, G Kasieczka, B Nachman, T Plehn SciPost Physics 10 (6), 139, 2021 | 86 | 2021 |
| Generative networks for precision enthusiasts A Butter, T Heimel, S Hummerich, T Krebs, T Plehn, A Rousselot, S Vent SciPost Physics 14 (4), 078, 2023 | 57 | 2023 |
| Measuring QCD splittings with invertible networks S Bieringer, A Butter, T Heimel, S Höche, U Köthe, T Plehn, ST Radev SciPost Physics 10 (6), 126, 2021 | 48 | 2021 |
| How to GAN event subtraction A Butter, T Plehn, R Winterhalder SciPost Physics Core 3 (2), 009, 2020 | 48 | 2020 |
| Generative Networks for LHC events A Butter, T Plehn Artificial intelligence for high energy physics, 191-240, 2022 | 46 | 2022 |
| How to GAN event unweighting M Backes, A Butter, T Plehn, R Winterhalder SciPost Physics 10 (4), 089, 2021 | 46 | 2021 |
| The machine learning landscape of top taggers, SciPost Phys A Butter arXiv preprint arXiv:1902.09914, 1902 | 45 | 1902 |
| Modern machine learning for LHC physicists T Plehn, A Butter, B Dillon, T Heimel, C Krause, R Winterhalder arXiv preprint arXiv:2211.01421, 2022 | 44 | 2022 |
| Calomplification—the power of generative calorimeter models S Bieringer, A Butter, S Diefenbacher, E Eren, F Gaede, D Hundhausen, ... Journal of Instrumentation 17 (09), P09028, 2022 | 41 | 2022 |
| An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training M Backes, A Butter, M Dunford, B Malaescu SciPost Physics Core 7 (1), 007, 2024 | 39 | 2024 |
| MadNIS-Neural multi-channel importance sampling T Heimel, R Winterhalder, A Butter, J Isaacson, C Krause, F Maltoni, ... SciPost Physics 15 (4), 141, 2023 | 37 | 2023 |
