no code implementations • 29 Apr 2024 • Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
no code implementations • 16 Dec 2022 • Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu
We introduce the iterative conditional INN~(IcINN) for unfolding that adjusts for deviations between simulated training samples and data.
no code implementations • 22 Oct 2021 • Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent
Generative networks are opening new avenues in fast event generation for the LHC.
no code implementations • 22 Dec 2020 • Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution High Energy Physics - Phenomenology
no code implementations • 14 Aug 2020 • Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.