Triton-3He relative and differential flows and the high density behavior of nuclear symmetry energy

Using a transport model coupled with a phase-space coalescence after-burner we study the triton-3He relative and differential transverse flows in semi-central 132Sn+124Sn reactions at a beam energy of 400 MeV/nucleon. We find that the triton-3He pairs carry interesting information about the density dependence of the nuclear symmetry energy. The t-3He relative flow can be used as a particularly powerful probe of the high-density behavior of the nuclear symmetry energy.Comment: 6 pages, 2 figures, Proceeding of The International Workshop on Nuclear Dynamics in Heavy-Ion Reactions and the Symmetry Energ

Information flow through a model of the C. elegans klinotaxis circuit

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm C. elegans. The models are grounded in the neuroanatomy and currently known neurophysiology of the worm. The unknown model parameters were optimized to reproduce the worm's behavior. Information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit's state-dependent response. (4) The neck carries non-uniform distribution about changes in concentration. Thus, not all directions of movement are equally informative. Each of these findings corresponds to an experimental prediction that could be tested in the worm to greatly refine our understanding of the neural circuit underlying klinotaxis. Information flow analysis also allows us to explore how information flow relates to underlying electrophysiology. Despite large variations in the neural parameters of individual circuits, the overall information flow architecture circuit is remarkably consistent across the ensemble, suggesting that information flow analysis captures general principles of operation for the klinotaxis circuit

Dynamics of Interacting Neural Networks

The dynamics of interacting perceptrons is solved analytically. For a directed flow of information the system runs into a state which has a higher symmetry than the topology of the model. A symmetry breaking phase transition is found with increasing learning rate. In addition it is shown that a system of interacting perceptrons which is trained on the history of its minority decisions develops a good strategy for the problem of adaptive competition known as the Bar Problem or Minority Game.Comment: 9 pages, 3 figures; typos corrected, content reorganize

High Density Behaviour of Nuclear Symmetry Energy and High Energy Heavy-Ion Collisions

High energy heavy-ion collisions are proposed as a novel means to obtain information about the high density ({\rm HD}) behaviour of nuclear symmetry energy. Within an isospin-dependent hadronic transport model using phenomenological equations of state ({\rm EOS}) for dense neutron-rich matter, it is shown that the isospin asymmetry of the HD nuclear matter formed in high energy heavy-ion collisions is determined mainly by the HD behaviour of nuclear symmetry energy. Experimental signatures in several sensitive probes, i.e., $\pi^-$ to $\pi^+$ ratio, transverse collective flow and its excitation function as well as neutron-proton differential flow, are investigated. A precursor of the possible isospin separation instability in dense neutron-rich matter is predicted to appear as the local minima in the excitation functions of the transverse flow parameter for both neutrons and protons above the pion production threshold. Because of its {\it qualitative} nature unlike other {\it quantitative} observables, this precursor can be used as a unique signature of the isospin dependence of the nuclear {\rm EOS}. Measurements of these observables will provide the first terrestrial data to constrain stringently the HD behaviour of nuclear symmetry energy and thus also the {\rm EOS} of dense neutron-rich matter. Implications of our findings to neutron star studies are also discussed.Comment: 25 pages + 16 figures, Nucl. Phys. A (2002) in pres

Gauge Theory for Finite-Dimensional Dynamical Systems

Gauge theory is a well-established concept in quantum physics, electrodynamics, and cosmology. This theory has recently proliferated into new areas, such as mechanics and astrodynamics. In this paper, we discuss a few applications of gauge theory in finite-dimensional dynamical systems with implications to numerical integration of differential equations. We distinguish between rescriptive and descriptive gauge symmetry. Rescriptive gauge symmetry is, in essence, re-scaling of the independent variable, while descriptive gauge symmetry is a Yang-Mills-like transformation of the velocity vector field, adapted to finite-dimensional systems. We show that a simple gauge transformation of multiple harmonic oscillators driven by chaotic processes can render an apparently "disordered" flow into a regular dynamical process, and that there exists a remarkable connection between gauge transformations and reduction theory of ordinary differential equations. Throughout the discussion, we demonstrate the main ideas by considering examples from diverse engineering and scientific fields, including quantum mechanics, chemistry, rigid-body dynamics and information theory