The fate of Cyg X-1: an empirical lower limit on BH-NS merger rate

The recent distance determination allowed precise estimation of the orbital parameters of Cyg X-1, which contains a massive 14.8 Msun BH with a 19.2 Msun O star companion. This system appears to be the clearest example of a potential progenitor of a BH-NS system. We follow the future evolution of Cyg X-1, and show that it will soon encounter a Roche lobe overflow episode, followed shortly by a Type Ib/c supernova and the formation of a NS. It is demonstrated that in majority of cases (70%) the supernova and associated natal kick disrupts the binary due to the fact that the orbit expanded significantly in the Roche lobe overflow episode. In the reminder of cases (30%) the newly formed BH-NS system is too wide to coalesce in the Hubble time. Only sporadically (1%) a Cyg X-1 like binary may form a coalescing BH-NS system given a favorable direction and magnitude of the natal kick. If Cyg X-1 like channel (comparable mass BH-O star bright X-ray binary) is the only or dominant way to form BH-NS binaries in the Galaxy we can estimate the empirical BH-NS merger rate in the Galaxy at the level of 0.001 per Myr. This rate is so low that the detection of BH-NS systems in gravitational radiation is highly unlikely, generating Advanced LIGO/VIRGO detection rates at the level of only 1 per century. If BH-NS inspirals are in fact detected, it will indicate that the formation of these systems proceeds via some alternative and yet unobserved channels.Comment: 5 pages, ApJ Letters (accepted

SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only $~$6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge

The Extreme Spin of the Black Hole in Cygnus X-1

The compact primary in the X-ray binary Cygnus X-1 was the first black hole to be established via dynamical observations. We have recently determined accurate values for its mass and distance, and for the orbital inclination angle of the binary. Building on these results, which are based on our favored (asynchronous) dynamical model, we have measured the radius of the inner edge of the black hole's accretion disk by fitting its thermal continuum spectrum to a fully relativistic model of a thin accretion disk. Assuming that the spin axis of the black hole is aligned with the orbital angular momentum vector, we have determined that Cygnus X-1 contains a near-extreme Kerr black hole with a spin parameter a/M>0.95 (3\sigma). For a less probable (synchronous) dynamical model, we find a/M>0.92 (3\sigma). In our analysis, we include the uncertainties in black hole mass, orbital inclination angle and distance, and we also include the uncertainty in the calibration of the absolute flux via the Crab. These four sources of uncertainty totally dominate the error budget. The uncertainties introduced by the thin-disk model we employ are particularly small in this case given the extreme spin of the black hole and the disk's low luminosity.Comment: Paper III of three papers on Cygnus X-1; 21 pages including 5 figures and 12 tables, ApJ in press. The paper is significantly restructured; two further tests of the robustness of our spin measurement are presented, and our error analysis has been substantially improved; the conclusions are unchange

Klein-Nishina effects on the high-energy afterglow emission of gamma-ray bursts

Extended high-energy(>100MeV) gamma-ray emission that lasts much longer than the prompt sub-MeV emission has been detected from quite a few gamma-ray bursts (GRBs) by Fermi Large Area Telescope (LAT) recently. A plausible scenario is that this emission is the afterglow synchrotron emission produced by electrons accelerated in the forward shocks. In this scenario, the electrons that produce synchrotron high-energy emission also undergo inverse-Compton (IC) loss and the IC scattering with the synchrotron photons should be in the Klein-Nishina regime. Here we study effects of the Klein-Nishina scattering on the high-energy synchrotron afterglow emission. We find that, at early times the Klein-Nishina suppression effect on those electrons that produce the high-energy emission is usually strong and therefore their inverse-Compton loss is small with a Compton parameter Y < a few for a wide range of parameter space. This leads to a relatively bright synchrotron afterglow at high energies that can be detected by Fermi LAT. As the Klein-Nishina suppression effect weakens with time, the inverse-Compton loss increases and could dominate over the synchrotron loss in some parameter space. This will lead to a faster temporal decay of the high-energy synchrotron emission than what is predicted by the standard synchrotron model, which may explain the observed rapid decay of the early high-energy gamma-ray emission in GRB090510 and GRB090902B.Comment: 8 page (emulateapj style), 8 figures, submitted to Ap

A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise