Possible High-Energy Neutrino and Photon Signals from Gravitational Wave Bursts due to Double Neutron Star Mergers

As the technology of gravitational-wave and neutrino detectors becomes increasingly mature, a multi-messenger era of astronomy is ushered in. Advanced gravitational wave detectors are close to making a ground-breaking discovery of gravitational wave bursts (GWBs) associated with mergers of double neutron stars (NS-NS). It is essential to study the possible electromagnetic (EM) and neutrino emission counterparts of these GWBs. Recent observations and numerical simulations suggest that at least a fraction of NS-NS mergers may leave behind a massive millisecond magnetar as the merger product. Here we show that protons accelerated in the forward shock powered by a magnetar wind pushing the ejecta launched during the merger process would interact with photons generated in the dissipating magnetar wind and emit high energy neutrinos and photons. We estimate the typical energy and fluence of the neutrinos from such a scenario. We find that $\sim$PeV neutrinos could be emitted from the shock front as long as the ejecta could be accelerated to a relativistic speed. The diffuse neutrino flux from these events, even under the most optimistic scenarios, is too low to account for the two events announced by the IceCube Collaboration, but it is only slightly lower than the diffuse flux of GRBs, making it an important candidate for the diffuse background of $\sim$PeV neutrinos. The neutron-pion decay of these events make them a moderate contributor to the sub-TeV gamma-ray diffuse background.Comment: Accepted for publication in PRD, minor revisio

Critical phenomena in gravitational collapse of Husain-Martinez-Nunez scalar field

We construct analytical models to study the critical phenomena in gravitational collapse of the Husain-Martinez-Nunez massless scalar field. We first use the cut-and-paste technique to match the conformally flat solution ($c=0$ ) onto an outgoing Vaidya solution. To guarantee the continuity of the metric and the extrinsic curvature, we prove that the two solutions must be joined at a null hypersurface and the metric function in Vaidya spacetime must satisfy some constraints. We find that the mass of the black hole in the resulting spacetime takes the form $M\propto (p-p^*)^\gamma$, where the critical exponent $\gamma$ is equal to $0.5$. For the case $c\neq 0$, we show that the scalar field must be joined onto two pieces of Vaidya spacetimes to avoid a naked singularity. We also derive the power-law mass formula with $\gamma=0.5$. Compared with previous analytical models constructed from a different scalar field with continuous self-similarity, we obtain the same value of $\gamma$. However, we show that the solution with $c\neq 0$ is not self-similar. Therefore, we provide a rare example that a scalar field without self-similarity also possesses the features of critical collapse.Comment: 14 pages, 6 figure

Bernstein type's concentration inequalities for symmetric Markov processes

Using the method of transportation-information inequality introduced in \cite{GLWY}, we establish Bernstein type's concentration inequalities for empirical means $\frac 1t \int_0^t g(X_s)ds$ where $g$ is a unbounded observable of the symmetric Markov process $(X_t)$. Three approaches are proposed : functional inequalities approach ; Lyapunov function method ; and an approach through the Lipschitzian norm of the solution to the Poisson equation. Several applications and examples are studied

Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination