23,280 research outputs found
Polarization of kilonova emission from a black hole-neutron star merger
A multi-messenger, black hole (BH) - neutron star (NS) merger event still
remains to be detected. The tidal (dynamical) ejecta from such an event,
thought to produce a kinonova, is concentrated in the equatorial plane and
occupies only part of the whole azimuthal angle. In addition, recent
simulations suggest that the outflow or wind from the post-merger remnant disk,
presumably anisotropic, can be a major ejecta component responsible for a
kilonova. For any ejecta whose photosphere shape deviates from the spherical
symmetry, the electron scattering at the photosphere causes a net polarization
in the kilonova light. Recent observational and theoretical polarization
studies have been focused to the NS-NS merger kilonova AT2017gfo. We extend
those work to the case of a BH-NS merger kilonova. We show that the degree of
polarization at the first hr can be up to 3\% if a small amount
() of free neutrons have survived in the fastest component
of the dynamical ejecta, whose beta-decay causes a precursor in the kilonova
light. The polarization degree can be 0.6\% if free neutrons survived in
the fastest component of the disk wind. Future polarization detection of a
kilonova will constrain the morphology and composition of the dominant ejecta
component, therefore help to identify the nature of the merger.Comment: 10 pages, 5 figures. Accepted for publication in Ap
SLT-Resolution for the Well-Founded Semantics
Global SLS-resolution and SLG-resolution are two representative mechanisms
for top-down evaluation of the well-founded semantics of general logic
programs. Global SLS-resolution is linear for query evaluation but suffers from
infinite loops and redundant computations. In contrast, SLG-resolution resolves
infinite loops and redundant computations by means of tabling, but it is not
linear. The principal disadvantage of a non-linear approach is that it cannot
be implemented using a simple, efficient stack-based memory structure nor can
it be easily extended to handle some strictly sequential operators such as cuts
in Prolog.
In this paper, we present a linear tabling method, called SLT-resolution, for
top-down evaluation of the well-founded semantics. SLT-resolution is a
substantial extension of SLDNF-resolution with tabling. Its main features
include: (1) It resolves infinite loops and redundant computations while
preserving the linearity. (2) It is terminating, and sound and complete w.r.t.
the well-founded semantics for programs with the bounded-term-size property
with non-floundering queries. Its time complexity is comparable with
SLG-resolution and polynomial for function-free logic programs. (3) Because of
its linearity for query evaluation, SLT-resolution bridges the gap between the
well-founded semantics and standard Prolog implementation techniques. It can be
implemented by an extension to any existing Prolog abstract machines such as
WAM or ATOAM.Comment: Slight modificatio
Stationary in Distributions of Numerical Solutions for Stochastic Partial Differential Equations with Markovian Switching
We investigate a class of stochastic partial differential equations with Markovian switching. By using the Euler-Maruyama scheme both in time and in space of mild solutions, we derive sufficient conditions for the existence and uniqueness of the stationary distributions of numerical solutions. Finally, one example is given to illustrate the theory
Anisotropic Flow and Viscous Hydrodynamics
We report part of our recent work on viscous hydrodynamics with consistent
phase space distribution f(x,\p) for freeze out. We develop the gradient
expansion formalism based on kinetic theory, and with the constraints from the
comparison between hydrodynamics and kinetic theory, viscous corrections to
f(x,\p) can be consistently determined order by order. Then with the obtained
f(x,\p), second order viscous hydrodynamical calculations are carried out for
elliptic flow .Comment: 8 pages, 2 figures. Proceedings for the 28th Winter Workshop on
Nuclear Dynamics, Dorado Del Mar, Puerto Rico, United States Of America, 7 -
14 Apr 201
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
- …