January 19, 2018
Deep Learning as a tool for Heavy Ion Physics
A Group of FIAS-Scientists used deep learning techniques to develop a tool for better understanding heavy ion collisions.
The present study is a proof of principle study where Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker and Xin-Nian Wang (University of California in Berkeley, USA) used more than 20.000 pictures from relativistic hydrodynamic simulations of heavy ion collisions in a convolution neural network (CNN) to classify two regions in the phase diagram.
"We started the project when a human professional was defeated in the game of Go against AlphaGo designed by Google Deepmind. The news ignited our enthusiasm and we discussed a lot on whether artificial intelligence can assist scientists to tackle challenging unsolved scientific problems." explains Long-Gang Pang, a former FIAS postdoc from Hannah Petersens group, who is now at the University of California in Berkeley, USA.
Publication: Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang: “An equation-of-state-meter of quantum chromodynamics transition from deep learning” Nature Communications https://www.nature.com/articles/s41467-017-02726-3, DOI : 10.1038/s41467-017-02726-3
