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 ($0^\circ$-$180^\circ$) of final spin-orbit misalignment angle from an initially aligned configuration. For systems that do not experience eccentricity excitation, only modest ($\lesssim 20^\circ$) 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

Design and Implementation of an RNS-based 2D DWT Processor

No abstract availabl