Related papers: Deep learning approaches to extract nuclear deform…
We present a systematic survey of even-even nuclei with $Z>50$ to identify where a very large prolate configuration driven by the second intruder orbit emerges. Within the energy density functional theory framework, we find in…
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent…
Recent work has provided the means to rigorously determine properties of super-hadronic matter from experimental data through the application of broad scale modeling of high-energy nuclear collisions within a Bayesian framework. These…
A detailed understanding of complete fusion cross sections in heavy-ion collisions requires a consideration of the effects of the deformation of the projectile and target. Our aim here is to show that deformation and orientation of the…
The deformation parameters beta and gamma together with the two-quasiparticle excitations are taken into account, for the first time, as coordinates within a symmetry conserving (angular momentum and particle number) generator coordinate…
We extend the well-studied midrapidity TRENTo initial-conditions model to three dimensions, thus facilitating (3+1)D modeling and analysis of ultrarelativistic heavy-ion collisions at RHIC and LHC energies. TRENTo-3D is a fast, parametric…
UU collisions can provide about 30% larger densities compared to central PbPb ones. New aspect is generation of rather deformed initial states. We show that those can be effectively used to resolve a number of outstanding issues, from…
Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle…
Observables from nuclear and high-energy experiments can be degraded by detector performance and/or methodology in extracting the observables, such as of the final-state characteristics of heavy-ion collisions in relation to a coarsely…
In relativistic heavy-ion collisions, the extractions of properties of quark-gluon plasma (QGP) are hindered by a limited understanding of its initial conditions, where the nuclear structure of the colliding ions play a significant role. In…
Nuclear shape and orientation degrees of freedom are incorporated into the calculation of the double-folding nuclear potential within the relativistic mean-field (RMF) formalism. The quadrupole deformations ($\beta_2$), nuclear densities…
In hydrodynamical modeling of heavy-ion collisions, the initial-state spatial anisotropies are translated into momentum anisotropies of the final-state particle distributions. Thus, understanding the origin of the initial-state anisotropies…
A detailed understanding of complete fusion cross sections in heavy-ion collisions requires a consideration of the effects of the deformation of the projectile and target. Our aim here is to show that deformation and orientation of the…
A new approach to the old problem of the predominance of prolate deformations among well deformed nuclei is proposed within the shell model framework. The parameter space is explored using the ensemble of random rotationally-invariant…
In this paper, we establish a new scheme for identification and classification of high intensity events generated by the propagation of light through a photorefractive SBN crystal. Among these events, which are the inevitable consequence of…
In this letter, we present the study of the role of octupole deformation in non-spherical nuclei in most-central Pb--Pb collisions at the LHC energy regime. The sensitivity of octupole deformation $\beta_3$ to the QGP observables is…
We investigate the possibility of selecting heavy ion collision events with certain features in the initial state ("event engineering"). Anisotropic flow measurements in heavy ion reactions have confirmed the almost ideal fluid dynamical…
We quantitatively estimate properties of the quark-gluon plasma created in ultra-relativistic heavy-ion collisions utilizing Bayesian statistics and a multi-parameter model-to-data comparison. The study is performed using a recently…
Four models for the initial conditions of a fluid dynamic description of high energy heavy ion collisions are analysed and compared. We study expectation values and event-by-event fluctuations in the initial transverse energy density…
A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amidst a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results…