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Cosmic ray (CR) feedback plays a vital role in shaping the formation and evolution of galaxies through their interaction with magnetohydrodynamic waves. In the CR self-confinement scenario, the waves are generated by the CR gyro-resonant instabilities via CR streaming or CR pressure anisotropy, and saturate by balancing wave damping. The resulting effective particle scattering rate by the waves, {\nu}eff, critically sets the coupling between the CRs and background gas, but the efficiency of CR feedback is yet poorly constrained. We employ 1D kinetic simulations under the Magnetohydrodynamic-Particle-In-Cell (MHD-PIC) framework with the adaptive {\delta}f method to quantify {\nu}eff for the saturated state of the CR pressure anisotropy instability (CRPAI) with ion-neutral friction. We drive CR pressure anisotropy by expanding/compressing box, mimicking background evolution of magnetic field strength, and the CR pressure anisotropy eventually reaches a quasi-steady state by balancing quasi-linear diffusion. At the saturated state, we measure {\nu}eff and the CR pressure anisotropy level, establishing a calibrated scaling relation with environmental parameters. The scaling relation is consistent with quasi-linear theory and can be incorporated to CR fluid models, in either the single-fluid or p-by-p treatments. Our results serve as a basis towards accurately calibrating the subgrid physics in macroscopic studies of CR feedback and transport., Comment: submitted to ApJ; 25 pages, 12 figures, comments welcomed

We present a new magnetohydrodynamic-particle-in-cell (MHD-PIC) code integrated into the Athena++ framework. It treats energetic particles as in conventional PIC codes while the rest of thermal plasmas are treated as background fluid described by MHD, thus primarily targeting at multi-scale astrophysical problems involving the kinetic physics of the cosmic-rays (CRs). The code is optimized toward efficient vectorization in interpolation and particle deposits, with excellent parallel scaling. The code is also compatible with static/adaptive mesh refinement, with dynamic load balancing to further enhance multi-scale simulations. In addition, we have implemented a compressing/expanding box framework which allows adiabatic driving of CR pressure anisotropy, as well as the $\delta f$ method that can dramatically reduce Poisson noise in problems where distribution function $f$ is only expected to slightly deviate from the background. The code performance is demonstrated over a series of benchmark test problems including particle acceleration in non-relativistic parallel shocks. In particular, we reproduce the linear growth of the CR gyro-resonant (streaming and pressure anisotropy) instabilities, under both the periodic and expanding/compressing box setting. We anticipate the code to open up the avenue for a wide range of astrophysical and plasma physics applications., Comment: 20 pages, 19 figures, submitted to MNRAS

Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model.

Hot accretion flows contain collisionless plasmas that are believed to be capable of accelerating particles to very high energies, as a result of turbulence generated by the magnetorotational instability (MRI). We conduct unstratified shearing-box simulations of the MRI turbulence in ideal magnetohydrodynamic (MHD), and inject energetic (relativistic) test particles in simulation snapshots to conduct a detailed investigation on particle diffusion and stochastic acceleration. We consider different amount of net vertical magnetic flux to achieve different disk magnetizations levels at saturated states, with sufficiently high resolution to resolve the gyro-radii ($R_g$) of most particles. Particles with large $R_g$ ($\gtrsim0.03$ disk scale height $H$) show spatial diffusion coefficients of $\sim30$ and $\sim5$ times Bohm values in the azimuthal and poloidal directions, respectively. We further measure particle momentum diffusion coefficient $D(p)$ by applying the Fokker-Planck equation to particle momentum evolution. For these particles, contribution from turbulent fluctuations scales as $D(p)\propto p$, and shear acceleration takes over when $R_g\gtrsim0.1H$, characterized by $D(p)\propto p^3$. For particles with smaller $R_g$ ($\lesssim0.03H$), their spatial diffusion coefficients roughly scale as $\sim p^{-1}$, and show evidence of $D(p)\propto p^2$ scaling in momentum diffusion but with large uncertainties. We find that multiple effects contribute to stochastic acceleration/deceleration, and the process is also likely affected by intermittency in the MRI turbulence. We also discuss the potential of accelerating PeV cosmic-rays in hot accretion flows around supermassive black holes., Comment: 22 pages, 18 figures, accepted for publication in MNRAS

We propose an on-chip Si photonics optical phased array based on a serial grating design which eliminates the use of directional couplers in previous designs. It significantly reduces overall phased array size especially when the number of the antenna is small which is often demanded in practice. The simulation results show our design reduces overall phased array size, increases optical power utilization while maintains comparable far field performance.

This paper presents a modified rotating element electric field vector (modified REV) method to calibrate the antenna array initial condition of an optical phased array (OPA) device. The new method follows a similar sequential individual antenna phase calibration process while it modifies the algorithm to avoid possible {\pi} phase error in traditional REV when large initial phase distribution and finite optical power measurement accuracy present. We show that the method produces statistically more accurate and predictable calibration result which is highly desired in practice.