Related papers: A comparative study of model ingredients: fragment…
The agreement between the fragments' internal and kinetic temperatures with the breakup temperature is investigated using a Statistical Multifragmentation Model which makes no a priori as- sumption on the relationship between them. We thus…
The features of heavy ion collisions that suggest the relevance of collective dynamics, as opposed to mere superpositions of nucleon-nucleon or even parton-parton collisions, are reviewed. The surprise of these studies is that bulk…
In our lecture we discuss the fermion models with quasilocal interaction implemented by derivatives and a momentum cutoff as substitutes of QCD at low energies. They are investigated in the strong coupling regime when several coupling…
Sparse latent multi-factor models have been used in many exploratory and predictive problems with high-dimensional multivariate observations. Because of concerns with identifiability, the latent factors are almost always assumed to be…
A quantum statistical model of nuclear multifragmentation is proposed. The recurrence equation method used within the canonical ensemble makes the model solvable and transparent to physical assumptions and allows to get results without…
Cluster production plays an important role in heavy-ion collisions at intermediate beam energies, where light nuclei contribute substantially to final-state yields and to other observables that are used to infer the nuclear equation of…
Using the quantum molecular dynamics model, we study the role of mass asymmetry of colliding nuclei on the fragmentation at the balance energy and on its mass dependence. The study is done by keeping the total mass of the system fixed as…
Fusion reactions of heavy ions are investigated by employing a simple stochastic semi-classical model which includes the coupling between relative motion and low frequency collective surface modes of colliding ions similarly to the quantal…
A variety of phenomena connected with the formation of a dinuclear complex is observed in the heavy ion collisions at low energies. The dinuclear system model allows us to analyze the experimental data and to interpret them by comparison of…
We apply the Bayesian model selection method (based on the Bayes factor) to optimize $\sqrt{s_\mathrm{NN}}$-dependence in the phenomenological parameters of the (3+1)-dimensional hybrid framework for describing relativistic heavy-ion…
The multifragmentation of excited spherical nuclear sources with various N/Z ratios and fixed mass number is studied within dynamical and statistical models. The dynamical model treats the multifragmentation process as a final stage of the…
We present theoretical approaches to high energy nuclear collisions in detail putting a special emphasis on technical aspects of numerical simulations. Models include relativistic hydrodynamics, Monte-Carlo implementation of…
An introduction to dynamical microscopic models of hadronic and nuclear interactions is presented. Special emphasis is put in the relation between multiparticle production and total cross-section contributions. In heavy ion collisions, some…
The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need of simulating multiple replicas of the…
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, directly from compositions and geometries of atomic…
The Boltzmann equation is a powerful theoretical tool for modeling the collective dynamics of quantum many-body systems subject to external perturbations. Analysis of the equation gives access to linear response properties including…
Multiplicity distributions of intermediate mass fragments (IMF) seen in intermediate energy heavy ion collisions have been of great interest in the last ten years. The distributions of single intermediate sized element are close to…
The elements of the quantum mechanical diffusion matrix, leading to a Gibbs equilibrium state for a set of $N$ coupled quantum harmonic oscillators are derived within Lindblad's axiomatic approach. Consequences of the fundamental…
We study the problem of learning Gaussian Mixture Models (GMMs) and ask: which structural properties govern their sample complexity? Prior work has largely tied this complexity to the minimum pairwise separation between components, but we…
Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular dynamics simulations to drug design and materials discovery. While recent approaches can…