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.
no code implementations • 12 May 2021 • Markus Schlott, Omar El Sayed, Mariia Bilousova, Fabian Hofmann, Alexander Kies, Horst Stöcker
Global warming is one of the main threats to the future of humanity and extensive emissions of greenhouse gases are found to be the main cause of global temperature rise as well as climate change.
no code implementations • 21 Jan 2021 • Alexander Kies, Bruno U. Schyska, Mariia Bilousova, Omar El Sayed, Jakub Jurasz, Horst Stöcker
We find significant differences between these datasets and cost-difference of about 10% result in the different energy mix.
Physics and Society
no code implementations • 15 Jan 2018 • Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions.
no code implementations • 13 Dec 2016 • Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$.