1 code implementation • 11 Sep 2023 • Erik Buhmann, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown
We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up.
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.
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
