Deep learning for flow observables in ultrarelativistic heavy-ion collisions

Abstract

We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, average transverse momenta and charged particle multiplicities in ultrarelativistic heavy-ion collisions from the initial energy density profiles. We show that the neural network can be trained accurately enough so that it can reliably predict the hydrodynamic results for the flow coefficients and, remarkably, also their correlations like normalized symmetric cumulants, mixed harmonic cumulants and flow-transverse-momentum correlations. At the same time the required computational time decreases by several orders of magnitude. To demonstrate the advantage of the significantly reduced computation time, we generate 10M initial energy density profiles from which we predict the flow observables using the neural network, which is trained using 5k, and validated using 90k events per collision energy. We then show that increasing the number of collision events from 90k to 10M can have significant effects on certain statistics-expensive flow correlations, which should be taken into account when using these correlators as constraints in the determination of the QCD matter properties.

Entanglement in High-Energy Physics: An Overview

“…In HEP, the study of quantum correlations becomes particularly fascinating in the context of extreme conditions, such as those found in the early universe or near black holes. Quantum entanglement may play a crucial role in unraveling the mysteries of cosmic inflation, the Big Bang, and the behavior of matter in ultra-high-energy collisions [48]. By harnessing the power of quantum correlations, researchers aim to gain deeper insights into the fundamental nature of particles and their interactions.…”