54,154 research outputs found
Spin-Orbit Misalignment of Merging Black-Hole Binaries with Tertiary Companions
We study the effect of external companion on the orbital and spin evolution
of merging black-hole (BH) binaries. An sufficiently close by and inclined
companion can excite Lidov-Kozai (LK) eccentricity oscillations in the binary,
thereby shortening its merger time. During such LK-enhanced orbital decay, the
spin axis of the BH generally exhibits chaotic evolution, leading to a wide
range (-) of final spin-orbit misalignment angle from an
initially aligned configuration. For systems that do not experience
eccentricity excitation, only modest () spin-orbit
misalignment can be produced, and we derive an analytic expression for the
final misalignment using the principle of adiabatic invariance. The spin-orbit
misalignment directly impacts the gravitational waveform, and can be used to
constrain the formation scenarios of BH binaries and dynamical influences of
external companions.Comment: Published in ApJ
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
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