arXiv subjects

H. Hirvonen

Publications and source records attributed to H. Hirvonen.

3 recordsLinked to original sources

arXiv2024

Deep learning for flow observables in high energy heavy-ion collisions

We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-transverse-momentum and charged particle multiplicity from the initial energy density profile. We show that this method provides results that are accurate enough, so that one can use neural networks to reliably estimate multi-particle flow correlators. Additionally, we train networks that can take any model parameter as an additional input and demonstrate with a few examples that the accuracy remains good. The usage of neural networks can reduce the computation time needed in performing Bayesian analyses with multi-particle flow correlators by many orders of magnitude.

hep-ph↗

arXiv2023

Flow correlations from a hydrodynamics model with dynamical freeze-out and initial conditions based on perturbative QCD and saturation

We extend the applicability of the hydrodynamics, perturbative QCD and saturation -based EKRT (Eskola-Kajantie-Ruuskanen-Tuominen) framework for ultrarelativistic heavy-ion collisions to peripheral collisions by introducing dynamical freeze-out conditions. As a new ingredient compared to the previous EKRT computations we also introduce a non-zero bulk viscosity. We compute various hadronic observables and flow correlations, including normalized symmetric cumulants, mixed harmonic cumulants and flow-transverse momentum correlations, and compare them against measurements from the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). We demonstrate that the inclusion of the dynamical freeze-out and bulk viscosity allows a better description of the measured flow coefficients in peripheral collisions and enables the use of an extended centrality range when constraining the properties of QCD matter in the future.

hep-ph↗

arXiv2023

Deep learning for flow observables in ultrarelativistic heavy-ion collisions

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.

hep-ph↗