arXiv subjects

G. -Q. Zhang

Publications and source records attributed to G. -Q. Zhang.

2 recordsLinked to original sources

arXiv2023

A study of the MAD accretion state across black hole spins for radiatively inefficient accretion flows

The study of Magnetically Arrested Disks (MAD) attract strong interest in recent years, as these disk configurations were found to generate strong jets as observed in many accreting systems. Here, we present the results of 14 general relativistic magnetohydrodynamic(GRMHD) simulations of advection dominated accretion flow in the MAD state across black hole spins, carried with cuHARM. Our main findings are as follows. (i) The jets transport a significant amount of angular momentum to infinity in the form of Maxwell stresses. For positive, high spin, the rate of angular momentum transport is about 5 times larger than for negative spin. This contribution is nearly absent for a non-rotating black hole. (ii) The mass accretion rate and the MAD parameter, both calculated at the horizon, are not correlated. However, their time derivatives are anti-correlated for every spin. (iii) For zero spin, the contribution of the toroidal component of the magnetic field to the magnetic pressure is negligible, while for fast spinning black hole, it is in the same order as the contribution of the radial magnetic component. For high positive spin, the toroidal component even dominates. (iv) For negative spins, the jets are narrower than their positive spin counterparts, while their fluctuations are larger. The weak jet from the non-rotating black hole is the widest with the smallest fluctuations. Our results highlight the complex, non-linear connection between the black hole spin and the resulting disk and jet properties in the MAD regime.

astro-ph.HE↗

arXiv2019

A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures

Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and a reliable automatic seizure/non-seizure classification method is needed. One of the challenges in automatic seizure/non-seizure classification is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) to exploit both spatial and temporal discriminating features and overcome seizure variabilities. The attention mechanism is to capture spatial features according to the contributions of different brain regions to seizures. The BiLSTM is to extract discriminating temporal features in the forward and the backward directions. Cross-validation experiments and cross-patient experiments over the noisy data of CHB-MIT are performed to evaluate our proposed approach. The obtained average sensitivity of 87.00%, specificity of 88.60% and precision of 88.63% in cross-validation experiments are higher than using the current state-of-the-art methods, and the standard deviations of our approach are lower. The evaluation results of cross-patient experiments indicate that, our approach has better performance compared with the current state-of-the-art methods and is more robust across patients.

cs.LG↗