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.
1 code implementation • 23 Aug 2023 • Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.
2 code implementations • 8 May 2023 • Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
2 code implementations • 3 Sep 2020 • Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih
We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.
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.
2 code implementations • 11 May 2020 • Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger
Accurate simulation of physical processes is crucial for the success of modern particle physics.
Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
