Related papers: Deep learning approaches to extract nuclear deform…
The density distributions of large nuclei are typically modeled with a Woods-Saxon distribution characterized by a radius $R_{0}$ and skin depth $a$. Deformation parameters $\beta$ are then introduced to describe non-spherical nuclei using…
The structure of heavy nuclei is difficult to disentangle in high-energy heavy-ion collisions. The deep convolution neural network (DCNN) might be helpful in mapping the complex final states of heavy-ion collisions to the nuclear structure…
In the framework of the Glauber approach we analyze the shape parameters of the early-formed system and their event-by-event fluctuations. We test a variety of models: the conventional wounded nucleon model, a model admixing binary…
Most atomic nuclei are deformed with a quadrupole shape described by its overall strength $\beta_2$ and triaxiality $\gamma$. The deformation can be accessed in high-energy heavy-ion collisions by measuring the collective flow response of…
This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to simulate intermediate-energy heavy-ion collisions between prolate nuclei $^{24}$Mg. The emphasis is on investigating the influence of centrality and orientation…
In heavy ion collisions, elliptic flow $v_2$ and radial flow, characterized by event-wise average transverse momentum $[p_{\mathrm{T}}]$, are related to the shape and size of the overlap region, which are sensitive to the shape of colliding…
In relativistic heavy-ion collisions, the radial flow of the fireball, usually characterized by transverse momentum spectra of final-state particles, encodes essential information about the hot and dense nuclear matter created in the…
Heavy-ion experiments provide a new opportunity to gain a deeper understanding of the structure of nuclei. To achieve this, it is crucial to identify observables under circumstances that are minimally affected by the process that leads to…
We present a novel ``imaging-by-smashing" approach for probing nuclear deformation in high-energy heavy-ion collisions. By analyzing anisotropic-flow ($v_n$) and mean transverse momentum ($\left[p_T\right]$)-based observables in collisions…
Recent studies have shown that the shape and radial profile of the colliding nuclei have strong influences on the initial condition of the heavy ion collisions and the subsequent development of the anisotropic flow. Using A Multi-Phase…
In the hydrodynamic framework of heavy-ion collisions, elliptic flow, $v_2$, is sensitive to the quadrupole deformation, $\beta$, of the colliding ions. This enables one to test whether the established knowledge on the low-energy structure…
The shape of atomic nuclei is often interpreted to possess a quadrupole deformation that fluctuates around some average profile. We investigate the impact of nuclear shape fluctuations on the initial state geometry in heavy ion collisions,…
Background: Nuclear deformation provides a crucial characteristic of nuclear structure. Conventionally, the quadrupole deformation length of a nucleus, $\delta_{2}$, has often been determined based on a macroscopic model through a deformed…
$\alpha$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and…
We analyze the nearest neighbor spacing distributions of low-lying 2+ levels of even-even nuclei. We grouped the nuclei into classes defined by the quadrupole deformation parameter (Beta2). We calculate the nearest neighbor spacing…
This work establishes a deep connection between two seemingly distant branches of nuclear physics: nuclear structure and relativistic heavy-ion collisions. At the heart of this connection is the recent discovery made at particle colliders…
Relativistic heavy ion collisions provide a unique opportunity to study the shape of colliding nuclei, even up to higher-order multiple deformations. In this work, several observables that are sensitive to quadrupole and hexadecapole…
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods.…
Event-by-event fluctuations in the initial conditions for a hydrodynamical description of heavy-ion collisions are characterized. We propose a Bessel-Fourier decomposition with respect to the azimuthal angle, the radius in the transverse…
The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For this purpose,…