English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Using deep convolutional neural network (CNN), the nature of the QCDtransition can be identified from the final-state pion spectra from hybridmodel simulations of heavy-ion collisions that combines a viscous hydrodynamicmodel with a hadronic cascade "after-burner". Two different types of equationsof state (EoS) of the medium are used in the hydrodynamic evolution. Theresulting spectra in transverse momentum and azimuthal angle are used as theinput data to train the neural network to distinguish different EoS. Differentscenarios for the input data are studied and compared in a systematic way. Aclear hierarchy is observed in the prediction accuracy when using theevent-by-event, cascade-coarse-grained and event-fine-averaged spectra as inputfor the network, which are about 80%, 90% and 99%, respectively. A comparisonwith the prediction performance by deep neural network (DNN) with only thenormalized pion transverse momentum spectra is also made. High-level featuresof pion spectra captured by a carefully-trained neural network were found to beable to distinguish the nature of the QCD transition even in a simulationscenario which is close to the experiments.

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning