CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

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.

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

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.

Shared Data and Algorithms for Deep Learning in Fundamental Physics

1 code implementation • 1 Jul 2021 • Lisa Benato, Erik Buhmann, Martin Erdmann, Peter Fackeldey, Jonas Glombitza, Nikolai Hartmann, Gregor Kasieczka, William Korcari, Thomas Kuhr, Jan Steinheimer, Horst Stöcker, Tilman Plehn, Kai Zhou

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.

BIG-bench Machine Learning Transfer Learning