arXiv subjects

Ye Shen

Publications and source records attributed to Ye Shen.

30 records · Page 2Linked to original sources

arXiv2024

Generally relativistic description of fast magnetic reconnection induced by thermal electromotive force

Many theoretical models were come up with to figure out the properties of magnetic reconnection process, among which the Sweet-Parker model is the most famous since it describes the magnetic reconnection in a concise way. However, the low reconnection rate expected by this model is generally not available in most astrophysical systems, which motivates people to seek fast reconnection models. Under the scheme of generalized magnetohydrodynamics (MHD) for pair plasma, a fast magnetic reconnection model was established, in which the thermal electromotive force plays a key role to remarkably increase the reconnection rate. In this work, I would like to extend the discussions in my previous work, about the generally relativistic description of Sweet-Parker model, to the description of fast magnetic reconnection induced by thermal electromotive force. I will revisit the fast reconnection model briefly to initialize my discussions and show how the thermal electromotive force impacts the reconnection rate. Next, some basic setups will be exhibited before discussing specific examples about how the properties of fast magnetic reconnection are modified by gravitational effect or in observations. Results in this work consolidate my opinion reiterated in my previous work that properties of magnetic reconnection would never be modified by gravitational effect significantly if the magnetic reconnection process occurs in a local scale while the modifications of properties could not be neglected when the process is detected by an observer who is moving with respect to the laboratory, in the rest frame of which the magnetic reconnection occurs.

physics.plasm-ph↗

arXiv2024

Energy extraction from a rotating black hole via magnetic reconnection: parameters in reconnection models

Works on the energy extraction from a rotating black hole via magnetic reconnection attract more attentions in recent years. Discussions on this topic, however, are often based on many simplifications, such as assuming a circularly flowing bulk plasma and a fixed orientation angle. A significant gap remains between theoretical models and the magnetic reconnection occurring in real astrophysical scenarios. In our previous work, we investigated the influence of orientation angle on energy extraction and figured out the differences between the plunging and circularly flowing bulk plasma. We introduced the concept of covering factor to quantify the capability of an accretion system in extracting energy via magnetic reconnection from a rotating black hole. In this work, as an improvement, we extend our discussions by treating the parameters in reconnection models as free parameters, bringing the theoretical model closer to the situations in real astrophysical systems. We employ two reconnection models, in which the geometric index and the guide field fraction are respectively induced. We separately present the dependences of energy extraction on the geometric index and the guide field fraction. More importantly, we propose to define the averaged covering factor weighted by reconnection rate to quantify the overall capability of an accretion system in extracting energy via magnetic reconnection from a rotating black hole, under the assumption that the magnetic reconnection process occurs stochastically and randomly within the ergosphere.

astro-ph.HE↗

arXiv2024

Precession and Split of Tilted, Geometrically Thin Accretion Disk: an Analytical Study

It has been observed that many relativistic jets display a kind of cork-screw-like precession. Numerical simulations has suggested that such kind of precession may originate from the precession of the disk. In this work, we introduce an analytical model to describe the precession and split of a tilted, geometrically thin disk. We consider the Lense-Thirring effect from the central (primary) black hole (BH) and the gravitational effect from the companion (secondary) BH far away from the center, both of which could induce the precession of the accretion disk around the spin axis of central black hole. We propose the splitting conditions that when the rate of viscous diffusion cannot catch up with the dynamical frequency at a certain layer of fluid, the disk would split into two parts which precess independently. We presume that the precessions of the inner and outer disks are in accord with the rotation and precession of jet, respectively. By matching the frequencies of the disks to the observed frequencies of jet in the cork-screw-like precession and considering the splitting condition, we are allowed to read four parameters, the innermost radius ($r_{\rm in}$), the outermost radius ($r_{\rm out}$) of the disk, the initial splitting radius ($r_{\rm sp,0}$), and the inflow speed magnitude($β$), of the disk. We apply this model to OJ 287. Moreover, considering the inward shrinking of the disks, we find the time variation of the precession angle of jet. This time variation presents a unique feature of our model, which could be distinguishable in the future observation.

astro-ph.HE↗

arXiv2024

Energy Extraction from a Kerr Black Hole via Magnetic Reconnection within the Plunging Region

Magnetic reconnection within a highly magnetized plasma has been seen as a viable mechanism to extract the energy from a rotating black hole, as it can generate negative energy plasmoids in the ergoregion. For a typical accreting black hole, the ergoregion is filled with bulk plasma plunging from the innermost-stable-circular orbit (ISCO). In this study, we present an analytical study of the energy extraction via magnetic reconnection process in the plunging region. In contrast to the toroidal plasma, where the magnetic field cannot be derived from the MHD scheme, the magnetic field in the plunging plasma was determined by the ideal-MHD condition. We derive the global magnetic field structure in a fast reconnection model, and we read the expressions for the energies of plasmoids ejected from the reconnection region, for general stationary and axisymmetric spacetimes. Then, we demonstrate the behaviors of ejected energies varying with the reconnection locations in the Kerr spacetime, and identify the region where a negative-energy plasmoid can be produced. We find that for a certain magnetization there exists a critical value of the black hole spin, beyond which the energy extraction can occur, and the energy extraction is most efficient for the near-extreme black hole. Moreover, we study the conditions necessary for a plasmoid with positive energy to escape to the infinity, a crucial requirement for effective energy extractions. Considering the escaping conditions, we provide the parameter space in the radius-spin plane in which the energy extraction mechanism is effective.

gr-qc↗

arXiv2024

Energy extraction from a rotating black hole via magnetic reconnection: the plunging bulk plasma and orientation angle

Magnetic reconection provides a new avenue to extract energy from a rotating black hole in astrophysical scenarios. There have been many works studying the feasibility of extracting energy via magnetic reconnection in the scheme of magnetohydrodynamics. However, most of them focused on circularly flowing bulk plasma only, and the influence of orientation angle, the angle between flowing direction of bulk plasma and ejection direction of plasmoids, was never carefully considered. In this work, we would like to study the energy extraction via magnetic reconnection, via the so-called Comisso-Asenjo process specifically, in the plunging plasma. More importantly, we analyze the influence of orientation angle in depth. Actually, we consider the magnetic reconnection occurring in bulk plasma with two kinds of streamlines, one being the circular flow and the other being the plunging flow from the ISCO. We find that the plunging plasma has higher energy-extraction efficiency. Moreover, we notice that it is favorable for energy extraction when the orientation angle is suitably increased if the bulk plasma plunges. We further define the covering factor. Under the assumption that the magnetic reconnection occurs equally probable along any direction on any position within ergosphere, this value help us to primitively judge the probability of energy extraction via magnetic reconnection, through which we conclude that the plunging bulk plasma is much more capable in energy extraction than the circularly flowing one.

gr-qc↗

arXiv2024

PharmacyGPT: The AI Pharmacist

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable insights into the potential applications and limitations of LLMs in the field of clinical pharmacy, with implications for both patient care and the development of future AI-driven healthcare solutions. By evaluating the performance of PharmacyGPT, we aim to contribute to the ongoing discourse surrounding the integration of artificial intelligence in healthcare settings, ultimately promoting the responsible and efficacious use of such technologies.

cs.CL↗

arXiv2024

How to describe the Sweet-Parker model in general relativity

It is a hot topic nowadays that magnetic reconnection, as a physical process to release magnetic energy effectively, occurs in numerous complicated astrophysical systems. Since the magnetic reconnection is thought to occur frequently in the accretion flow around compact objects which induce strong gravitational field, it is now regarded to be a practical mechanism to extract energy from rotation black holes, which motivates people to consider how to describe the process of magnetic reconnection in a generally relativistic way. In this work, I try to explore the description of Sweet-Parker model, one of the most famous theoretical models of magnetic reconnection, in general relativity. I begin with revisiting the Sweet-Parker model in special relativity and reorganize the calculations in seven steps, whose generally relativistic forms are discussed. I propose in this work, from the general discussions and consequences of specific examples, that no property in Sweet-Parker model would be modified by spacetime curvature, which is opposite to the conclusions in previous work. However, on the contrary, observation in different rest frames may bring modifications. If the magnetic reconnection occurs not in the rest frame of observer, the observer would find out that the detected relation between the reconnection rate and Lundquist number or that between outflow speed and Alfven velocity are not the same as the detected relations if the magnetic reconnection occurs just in the rest frame of observer.

gr-qc↗

arXiv2024

Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning

Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instructions on the early-stop of the online experiment and timely feedback from the environment. Policy evaluation in online learning thus attracts increasing attention by inferring the mean outcome of the optimal policy (i.e., the value) in real-time. Yet, such a problem is particularly challenging due to the dependent data generated in the online environment, the unknown optimal policy, and the complex exploration and exploitation trade-off in the adaptive experiment. In this paper, we aim to overcome these difficulties in policy evaluation for online learning. We explicitly derive the probability of exploration that quantifies the probability of exploring non-optimal actions under commonly used bandit algorithms. We use this probability to conduct valid inference on the online conditional mean estimator under each action and develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy in online learning. The proposed value estimator provides double protection for consistency and is asymptotically normal with a Wald-type confidence interval provided. Extensive simulation studies and real data applications are conducted to demonstrate the empirical validity of the proposed DREAM method.

stat.ML↗

arXiv2023

Imaging a Semi-Analytical Jet model Generated by 3D GRMHD Simulation

Employing 3D GRMHD simulation, we study the images of a geometrically thin jet, whose emissions concentrate on its surface, for accretion system surrounding a central spinning BH. By introducing a strong magnetic field, we observe three phases of BH accretion evolution: (a) initially, both the accretion rate and the magnetic flux on the horizon gradually increase; (b) at an intermediate stage, the magnetic flux approximately reaches saturation, and a jet forms via the Blandford-Znajek (BZ) mechanism; (c) ultimately, the entire system achieves a dynamic equilibrium, and a magnetically arrested disk (MAD) forms. We carefully study the jet images during the saturation and MAD regimes at various frequencies and from different observational angles. We reveal the presence of U-shaped brighter lines near the jet surface boundaries, which can be attributed to the photons whose trajectories skim over the jet surface. The existence of these brighter lines is a unique feature of a geometrically thin jet. Moreover, we notice that the jet images are relatively insensitive to the observed frequencies of interest. Additionally, we observe that the time-averaged images for the highly oscillating MAD regime show only slight differences from those of the saturation regime.

astro-ph.HE↗

arXiv2023

AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance maps visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes.

cs.CL↗

arXiv2023

Heterogeneous Synthetic Learner for Panel Data

In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.

stat.ML↗

arXiv2022

Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process

This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process. Based on this result, we develop an efficient off-policy value estimator that is robust to potential model misspecification and provide rigorous uncertainty quantification. Our method is justified by theoretical results, simulated and real datasets obtained from ridesharing companies. A Python implementation of the proposed procedure is available at https://github.com/Mamba413/cope.

stat.ML↗