No Arabic abstract
Internal dynamical evolution can drive stellar systems into states of high central density. For many star clusters and galactic nuclei, the time scale on which this occurs is significantly less than the age of the universe. As a result, such systems are expected to be sites of frequent interactions among stars, binary systems, and stellar remnants, making them efficient factories for the production of compact binaries, intermediate-mass black holes, and other interesting and eminently observable astrophysical exotica. We describe some elements of the competition among stellar dynamics, stellar evolution, and other mechanisms to control the dynamics of stellar systems, and discuss briefly the techniques by which these systems are modeled and studied. Particular emphasis is placed on pathways leading to massive black holes in present-day globular clusters and other potentially detectable sources of gravitational radiation.
Strong gravitational lensing and stellar dynamics provide two complementary and orthogonal constraints on the density profiles of galaxies. Based on spherically symmetric, scale-free, mass models, it is shown that the combination of both techniques is powerful in breaking the mass-sheet and mass-anisotropy degeneracies. Second, observational results are presented from the Lenses Structure & Dynamics (LSD) Survey and the Sloan Lens ACS (SLACS) Survey collaborations to illustrate this new methodology in constraining the dark and stellar density profiles, and mass structure, of early-type galaxies to redshifts of unity.
The last fifteen years have seen the discovery of new types of low-mass stellar systems that bridge the gap between the once well-separated regimes of galaxies and of star clusters. Whether such objects are considered galaxies depends also on the definition of the term galaxy, and several possible criteria are based on their internal dynamics (e.g. the common concept that galaxies contain dark matter). Moreover, studying the internal dynamics of low-mass stellar systems may also help understand their origin and evolutionary history. The focus of this paper is on two classes of stellar systems at the interface between star clusters and dwarf galaxies: ultra-compact dwarf galaxies (UCDs) and diffuse Galactic globular clusters (GCs). A review of our current knowledge on the properties of UCDs is provided and dynamical considerations applying to diffuse GCs are introduced. In the following, recent observational results on the internal dynamics of individual UCDs and diffuse Galactic globular clusters are presented.
Due to the exponential growth of the state space of coupled quantum systems it is not possible, in general, to numerically store the state of a very large number of quantum systems within a classical computer. We demonstrate a method for modelling the dynamical behaviour of measurable quantities for very large numbers of interacting quantum systems. Our approach makes use of a symbolic non-commutative algebra engine that we have recently developed in conjunction with the well-known Ehrenfest theorem. Here we show the possibility of determining the dynamics of experimentally observable quantities, without approximation, for very large numbers of interacting harmonic oscillators. Our analysis removes a large number of significant constraints present in previous analysis of this example system (such as having no entanglement in the initial state). This method will be of value in simulating the operation of large quantum machines, emergent behaviour in quantum systems, open quantum systems and quantum chemistry to name but a few.
Understanding the mechanisms of complex systems is very important. Networked dynamical system, that understanding a system as a group of nodes interacting on a given network according to certain dynamic rules, is a powerful tool for modelling complex systems. However, finding such models according to the time series of behaviors is hard. Conventional methods can work well only on small networks and some types of dynamics. Based on a Bernoulli network generator and a Markov dynamics learner, this paper proposes a unified framework for Automated Interaction network and Dynamics Discovery (AIDD) on various network structures and different types of dynamics. The experiments show that AIDD can be applied on large systems with thousands of nodes. AIDD can not only infer the unknown network structure and states for hidden nodes but also can reconstruct the real gene regulatory network based on the noisy, incomplete, and being disturbed data which is closed to real situations. We further propose a new method to test data-driven models by experiments of control. We optimize a controller on the learned model, and then apply it on both the learned and the ground truth models. The results show that both of them behave similarly under the same control law, which means AIDD models have learned the real network dynamics correctly.
We study the dynamics of the statistics of the energy transferred across a point along a quantum chain which is prepared in the inhomogeneous initial state obtained by joining two identical semi-infinite parts thermalized at two different temperatures. In particular, we consider the transverse field Ising and harmonic chains as prototypical models of non-interacting fermionic and bosonic excitations, respectively. Within the so-called hydrodynamic limit of large space-time scales we first discuss the mean values of the energy density and current, and then, aiming at the statistics of fluctuations, we calculate exactly the scaled cumulant generating function of the transferred energy. From the latter, the evolution of the associated large deviation function is obtained. A natural interpretation of our results is provided in terms of a semi-classical picture of quasi-particles moving ballistically along classical trajectories. Similarities and differences between the transferred energy scaled cumulant and the large deviation functions in the cases of non-interacting fermions and bosons are discussed.