From the 1 of 5 linked papers with an AI index.
5 papers
Neural network enhanced Bayesian global analysis of relativistic heavy ion collisions
Jussi Auvinen, Kari J. Eskola, Henry Hirvonen +1
The paper presents a deep convolutional neural network that emulates costly viscous hydrodynamic simulations, enabling a Bayesian global analysis of bulk observables from relativis…
Can high- theory and data constrain ?
Bithika Karmakar, Dusan Zigic, Igor Salom +4
Understanding the temperature dependence of the specific shear viscosity is crucial for characterizing the properties of the QCD matter produced in ultrarelativistic heavy…
MC-EKRT: Monte Carlo event generator with saturated minijet production for initializing 3+1 D fluid dynamics in high energy nuclear collisions
Harri Niemi, Jussi Auvinen, Kari J. Eskola +3
We present a novel saturation and leading order collinear factorization based Monte-Carlo implementation of the EKRT model for computing QCD matter initial states in high-energy nu…
Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions
Henry Hirvonen, Mikko Kuha, Jussi Auvinen +3
We investigate the effects of saturation dynamics on midrapidity flow observables by adding fluctuating hotspots into the novel Monte Carlo EKRT (MC-EKRT) event generator for high-…
MC-EKRT: Monte Carlo event generator with saturated minijet production for initializing 3+1 D fluid dynamics in high energy nuclear collisions
Mikko Kuha, Jussi Auvinen, Kari J. Eskola +3
We present a novel Monte-Carlo implementation of the EKRT model, MC-EKRT, for computing partonic initial states in high-energy nuclear collisions. Our new MC-EKRT event generator i…