Investigation of a Magnetic Reconnection Event with Extraordinarily High Particle Energization in Magnetotail Turbulence
Astrophysical Journal Letters(2024)SCI 2区
Lab Atmospher & Space Phys
Abstract
Magnetic reconnection and plasma turbulence are ubiquitous and key processes in the Universe. These two processes are suggested to be intrinsically related: magnetic reconnection can develop turbulence, and, in turn, turbulence can influence or excite magnetic reconnection. In this study, we report a rare and unique electron diffusion region (EDR) observed by the Magnetospheric Multiscale mission in the Earth’s magnetotail with significantly enhanced energetic particle fluxes. The EDR is in a region of strong turbulence within which the plasma density is dramatically depleted. We present three salient features. (1) Despite the turbulence, the EDR behaves nearly the same as that in 2D quasi-planar reconnection; the observations suggest that magnetic reconnection continues for several minutes. (2) The observed reconnection electric field and inferred energy transport are exceptionally large. However, the aspect ratio of the EDR (one definition of reconnection rate) is fairly typical. Instead, extraordinarily large-amplitude Hall electric fields appear to enable the strong energy transport. (3) We hypothesize that the high-energy transport rate, density depletion, and the strong particle acceleration are related to a near-runaway effect, which is due to the combination of low-plasma-density inflow (from lobes) and possible positive feedback between turbulence and reconnection. The detailed study on this EDR gives insight into the interplay between reconnection and turbulence, and the possible near-runaway effect, which may play an important role in other particle acceleration in astrophysical plasma.
MoreTranslated text
Key words
Plasma physics,Space plasmas
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
ASTROPHYSICAL JOURNAL LETTERS 2025
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话