Stochastic gravitational-wave background from spin loss of black holes

Although spinning black holes are shown to be stable in vacuum in general relativity, there exists exotic mechanisms that can convert the spin energy of black holes into gravitational waves. Such waves may be very weak in amplitude, since the spin-down could take a long time, and a direct search may not be feasible. We propose to search for the stochastic background associated with the spin-down, and we relate the level of this background to the formation rate of spinning black holes from the merger of binary black holes, as well as the energy spectrum of waves emitted by the spin-down process. We argue that current LIGO-Virgo observations are not inconsistent with the existence of a spin-down process, as long as it is slow enough. On the other hand, the background may still exist as long as a moderate fraction of spin energy is emitted within Hubble time. This stochastic background could be one interesting target of next generation GW detector network, such as LIGO Voyager, and could be extracted from total stochastic background

Inequalities for selected eigenvalues of the product of matrices

The product of a Hermitian matrix and a positive semidefinite matrix has only real eigenvalues. We present bounds for sums of eigenvalues of such a product.Comment: to appear in AMS Proceeding

Global Gevrey hypoellipticity for the twisted Laplacian on forms

We study in this paper the global hypoellipticity property in the Gevrey category for the generalized twisted Laplacian on forms. Different from the 0-form case, where the twisted Laplacian is a scalar operator, this is a system of differential operators when acting on forms, each component operator being elliptic locally and degenerate globally. We obtain here the global hypoellipticity in anisotropic Gevrey space

Analysis on predict model of railway passenger travel factors judgment with soft-computing methods

Purpose: With the development of the transportation, more traveling factors acting on the railway passengers change greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel purpose and travel cost. Design/methodology/approach: The detailed comparative study was implemented with comparing the two soft-computing methods: genetic algorithm, BP neural network. The two methods with different idea were also studied in this model to discuss the key parameter setting and its applicable range. Findings: During the study, the data about the railway passengers is difficult to analyzed detailed because of the inaccurate information. There are still many factors to affect the choice of passengers. Research limitations/implications: The model-designing thought and its computing procession were also certificated with programming and data illustration according to thorough analysis. The comparative analysis was also proved effective and applicable to predict the railway passengers’ travel choice through the empirical study with soft-computing supporting. Practical implications: The techniques of predicting and parameters’ choice were conducted with algorithm-operation supporting. Originality/value: The detail form comparative study in this paper could be provided for researchers and managers and be applied in the practice according the actual demand.Peer Reviewe