Nuclear Three-body Force Effect on a Kaon Condensate in Neutron Star Matter

We explore the effects of a microscopic nuclear three-body force on the threshold baryon density for kaon condensation in chemical equilibrium neutron star matter and on the composition of the kaon condensed phase in the framework of the Brueckner-Hartree-Fock approach. Our results show that the nuclear three-body force affects strongly the high-density behavior of nuclear symmetry energy and consequently reduces considerably the critical density for kaon condensation provided that the proton strangeness content is not very large. The dependence of the threshold density on the symmetry energy becomes weaker as the proton strangeness content increases. The kaon condensed phase of neutron star matter turns out to be proton-rich instead of neutron-rich. The three-body force has an important influence on the composition of the kaon condensed phase. Inclusion of the three-body force contribution in the nuclear symmetry energy results in a significant reduction of the proton and kaon fractions in the kaon condensed phase which is more proton-rich in the case of no three-body force. Our results are compared to other theoretical predictions by adopting different models for the nuclear symmetry energy. The possible implications of our results for the neutron star structure are also briefly discussed.Comment: 15 pages, 5 figure

Resonance model study of kaon production in baryon baryon reactions for heavy ion collisions

The energy dependence of the total kaon production cross sections in baryon baryon ($N$ and $\Delta$) collisions are studied in the resonance model, which is a relativistic, tree-level treatment. This study is the first attempt to complete a systematic, consistent investigation of the elementary kaon production reactions for both the pion baryon and baryon baryon reactions. Our model suggests that the magnitudes of the isospin-averaged total cross sections for the $N N \to N Y K$ and $\Delta N \to N Y K$ ($Y = \Lambda$ or $\Sigma$) reactions are almost equal at energies up to about 200 MeV above threshold. However, the magnitudes for the $\Delta N$ reactions become about 6 times larger than those for the $N N$ reactions at energies about 1 GeV above threshold. Furthermore, the magnitudes of the isospin-averaged total cross sections for the $N N \to \Delta Y K$ reactions turn out to be comparable to those for the $N N \to N Y K$ reactions at $N N$ invariant collision energies about 3.1 GeV, and about 5 to 10 times larger at $N N$ invariant collision energies about 3.5 GeV. The microscopic cross sections are parametrized in all isospin channels necessary for the transport model studies of kaon production in heavy ion collisions. These cross sections are then applied in the relativistic transport model to study the sensitivity to the underlying elementary kaon production cross sections.Comment: Latex, 47 pages, 23 postscript figures. Typos in the published version, which informed as errata to the editor, are corrected for the use of simulation cod

Spectral Lags Obtained by CCF of Smoothed Lightcurves

We present a new technique to calculate the spectral lags of gamma-ray bursts (GRBs). Unlike previous processing methods, we first smooth the light curves of gamma-ray bursts in high and low energy bands using the "Loess" filter, then, we directly define the spectral lags as such to maximize the cross-correlation function (CCF) between two smoothed light curves. This method is suitable for various shapes of CCF; it effectively avoids the errors caused by manual selections for the fitting function and fitting interval. Using the method, we have carefully measured the spectral lags of individual pulses contained in BAT/Swift gamma-ray bursts with known redshifts, and confirmed the anti-correlation between the spectral lag and the isotropy luminosity. The distribution of spectral lags can be well fitted by four Gaussian components, with the centroids at 0.03 s, 0.09 s, 0.15 s, and 0.21 s, respectively. We find that some spectral lags of the multi-peak GRBs seem to evolve with time

Modeling of fibrous biological tissues with a general invariant that excludes compressed fibers

Dispersed collagen fibers in fibrous soft biological tissues have a significant effect on the overall mechanical behavior of the tissues. Constitutive modeling of the detailed structure obtained by using advanced imaging modalities has been investigated extensively in the last decade. In particular, our group has previously proposed a fiber dispersion model based on a generalized structure tensor. However, the fiber tension–compression switch described in that study is unable to exclude compressed fibers within a dispersion and the model requires modification so as to avoid some unphysical effects. In a recent paper we have proposed a method which avoids such problems, but in this present study we introduce an alternative approach by using a new general invariant that only depends on the fibers under tension so that compressed fibers within a dispersion do not contribute to the strain-energy function. We then provide expressions for the associated Cauchy stress and elasticity tensors in a decoupled form. We have also implemented the proposed model in a finite element analysis program and illustrated the implementation with three representative examples: simple tension and compression, simple shear, and unconfined compression on articular cartilage. We have obtained very good agreement with the analytical solutions that are available for the first two examples. The third example shows the efficacy of the fibrous tissue model in a larger scale simulation. For comparison we also provide results for the three examples with the compressed fibers included, and the results are completely different. If the distribution of collagen fibers is such that it is appropriate to exclude compressed fibers then such a model should be adopted

Hot Nuclear Matter Equation of State with a Three-body Force

The finite temperature Brueckner-Hartree-Fock approach is extended by introducing a microscopic three-body force. In the framework of the extended model, the equation of state of hot asymmetric nuclear matter and its isospin dependence have been investigated. The critical temperature of liquid-gas phase transition for symmetric nuclear matter has been calculated and compared with other predictions. It turns out that the three-body force gives a repulsive contribution to the equation of state which is stronger at higher density and as a consequence reduces the critical temperature of liquid-gas phase transition. The calculated energy per nucleon of hot asymmetric nuclear matter is shown to satisfy a simple quadratic dependence on asymmetric parameter $\beta$ as in the zero-temperature case. The symmetry energy and its density dependence have been obtained and discussed. Our results show that the three-body force affects strongly the high-density behavior of the symmetry energy and makes the symmetry energy more sensitive to the variation of temperature. The temperature dependence and the isospin dependence of other physical quantities, such as the proton and neutron single particle potentials and effective masses are also studied. Due to the additional repulsion produced by the three-body force contribution, the proton and neutron single particle potentials are correspondingly enhanced as similar to the zero-temperature case.Comment: 16 pages, 8 figure

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure