The influence of boundary conditions on the distribution of energetic electrons during collisionless magnetic reconnection

Lei WANG; Can HUANG; Dongke CHEN; Zhongwei YANG; Aimin DU; Yasong GE

doi:10.1088/2058-6272/ad0d5a

Plasma Science and Technology > 2024 > 26(4): 045301. > DOI: 10.1088/2058-6272/ad0d5a

Lei WANG, Can HUANG, Dongke CHEN, Zhongwei YANG, Aimin DU, Yasong GE. The influence of boundary conditions on the distribution of energetic electrons during collisionless magnetic reconnection[J]. Plasma Science and Technology, 2024, 26(4): 045301. DOI: 10.1088/2058-6272/ad0d5a

Citation:

PDF (1244 KB)

The influence of boundary conditions on the distribution of energetic electrons during collisionless magnetic reconnection

1.
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, People’s Republic of China
2.
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100029, People’s Republic of China
3.
State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

More Information

Author Bio:
Can HUANG: huangcan@mail.iggcas.ac.cn
Corresponding author:
Can HUANG, huangcan@mail.iggcas.ac.cn
Received Date: August 20, 2023
Revised Date: November 01, 2023
Accepted Date: November 02, 2023
Available Online: April 08, 2024
Published Date: April 07, 2024

Graphical Abstract

Abstract

Abstract

We conducted 2-D particle-in-cell simulations to investigate the impact of boundary conditions on the evolution of magnetic reconnection. The results demonstrate that the boundary conditions are crucial to this evolution. Specifically, in the cases of traditional periodic boundary (PB) and fully-opened boundary (OB) conditions, the evolutions are quite similar before the system achieves the fastest reconnection rate. However, differences emerge between the two cases afterward. In the PB case, the reconnection electric field experiences a rapid decline and even becomes negative, indicating a reversal of the reconnection process. In contrast, the system maintains a fast reconnection stage in the OB case. Suprathermal electrons are generated near the separatrix and in the exhaust region of both simulation cases. In the electron density depletion layer and the dipolarization front region, a larger proportion of suprathermal electrons are produced in the OB case. Medium-energy electrons are mainly located in the vicinity of the X-line and downstream of the reconnection site in both cases. However, in the OB case, they can also be generated in the electron holes along the separatrix. Before the reverse reconnection stage, no high-energy electrons are present in the PB case. In contrast, about 20% of the electrons in the thin and elongated electron current layer are high-energy in the OB case.
- magnetic reconnection,
- electron energization,
- particle-in-cell simulation

FullText(HTML)

Acknowledgments

We acknowledge the support from the Key Research Program of the Chinese Academy of Sciences (No. ZDBS-SSW-TLC00105), the National Key R&D Program of China (No. 2022YFF0503200), National Natural Science Foundation of China (Nos. 41974173 and 42274224), and the Youth Innovation Promotion Association, Chinese Academy of Sciences (No. 2019066).

References (58)

References

[1]	Yamada M, Kulsrud R and Ji H T 2010 Rev. Mod. Phys. 82 603 doi: 10.1103/RevModPhys.82.603
[2]	Paschmann G, Øieroset M and Phan T 2013 Space Sci. Rev. 178 385 doi: 10.1007/s11214-012-9957-2
[3]	Biskamp D 1996 Astrophys. Space Sci. 242 165 doi: 10.1007/BF00645113
[4]	Hesse M and Cassak P A 2020 J. Geophys. Res. Space Phys. 125 e2018JA025935 doi: 10.1029/2018JA025935
[5]	Torbert R B et al 2018 Science 362 1391 doi: 10.1126/science.aat2998
[6]	Lu Q M et al 2022 Chin. Phys. B 31 089401 doi: 10.1088/1674-1056/ac76ab
[7]	Shibata K et al 1995 Astrophys. J. 451 L83
[8]	Hurley K et al 2005 Nature 434 1098 doi: 10.1038/nature03519
[9]	Lin J and Forbes T G 2000 J. Geophys. Res. Space Phys. 105 2375 doi: 10.1029/1999JA900477
[10]	Angelopoulos V et al 2008 Science 321 931 doi: 10.1126/science.1160495
[11]	Du A M et al 2011 J. Geophys. Res. Space Phys. 116 A03216
[12]	Cao J B et al 2013 J. Geophys. Res. Space Phys. 118 313 doi: 10.1029/2012JA018351
[13]	Fu H S et al 2013 Nat. Phys. 9 426 doi: 10.1038/nphys2664
[14]	Hastie R J 1997 Astrophys. Space Sci. 256 177 doi: 10.1023/A:1001728227899
[15]	Yamada M et al 1997 Phys. Rev. Lett. 78 3117 doi: 10.1103/PhysRevLett.78.3117
[16]	Sang L L et al 2022 Phys. Plasmas 29 102108 doi: 10.1063/5.0090790
[17]	Li X M et al 2022 Nat. Commun. 13 3241 doi: 10.1038/s41467-022-31025-9
[18]	Lu S et al 2020 Nat. Commun. 11 5049 doi: 10.1038/s41467-020-18787-w
[19]	Phan T D et al 2018 Nature 557 202 doi: 10.1038/s41586-018-0091-5
[20]	Hoshino M 2005 J. Geophys. Res. Space Phys. 110 A10215
[21]	Egedal J, Le A and Daughton W 2013 Phys. Plasmas 20 061201 doi: 10.1063/1.4811092
[22]	Egedal J, Daughton W and Le A 2012 Nat. Phys. 8 321 doi: 10.1038/nphys2249
[23]	Drake J F et al 2005 Phys. Rev. Lett. 94 095001 doi: 10.1103/PhysRevLett.94.095001
[24]	Fu X R, Lu Q M and Wang S 2006 Phys. Plasmas 13 012309 doi: 10.1063/1.2164808
[25]	Huang C, Lu Q M and Wang S 2010 Phys. Plasmas 17 072306 doi: 10.1063/1.3457930
[26]	Huang C et al 2015 J. Geophys. Res. Space Phys. 120 1759 doi: 10.1002/2014JA020918
[27]	Huang K et al 2021 J. Geophys. Res. Space Phys. 126 e2021JA029939 doi: 10.1029/2021JA029939
[28]	Wu M Y et al 2013 J. Geophys. Res. Space Phys. 118 4804 doi: 10.1002/jgra.50456
[29]	Fu H S et al 2011 Geophys. Res. Lett. 38 L16104
[30]	Wang R S et al 2010 Phys. Rev. Lett. 104 175003 doi: 10.1103/PhysRevLett.104.175003
[31]	Oka M et al 2010 Astrophys. J. 714 915 doi: 10.1088/0004-637X/714/1/915
[32]	Drake J F et al 2006 Nature 443 553 doi: 10.1038/nature05116
[33]	Wang R S et al 2016 Nat. Phys. 12 263 doi: 10.1038/nphys3578
[34]	Wang H Y et al 2016 Astrophys. J. 821 84 doi: 10.3847/0004-637X/821/2/84
[35]	Lu Q M et al 2018 Phys. Plasmas 25 072126 doi: 10.1063/1.5034012
[36]	Oka M et al 2010 J. Geophys. Res. Space Phys. 115 A08223
[37]	Hoshino M 2012 Phys. Rev. Lett. 108 135003 doi: 10.1103/PhysRevLett.108.135003
[38]	Huang C et al 2017 Astrophys. J. 835 245 doi: 10.3847/1538-4357/835/2/245
[39]	Wang S M et al 2020 Nat. Commun. 11 3964 doi: 10.1038/s41467-020-17803-3
[40]	Wang S M et al 2023 Geophys. Res. Lett. 50 e2023GL103203 doi: 10.1029/2023GL103203
[41]	Shay M A et al 1999 Geophys. Res. Lett. 26 2163 doi: 10.1029/1999GL900481
[42]	Hesse M, Birn J and Kuznetsova M 2001 J. Geophys. Res. Space Phys. 106 3721 doi: 10.1029/1999JA001002
[43]	Pritchett P L 2001 J. Geophys. Res. Space Phys. 106 3783 doi: 10.1029/1999JA001006
[44]	Daughton W, Scudder J and Karimabadi H 2006 Phys. Plasmas 13 072101 doi: 10.1063/1.2218817
[45]	Huang C, Du A M and Ge Y S 2020 Plasma Phys. Control. Fusion 62 055014 doi: 10.1088/1361-6587/ab7d49
[46]	Birdsall C K and Langdon A B 1991 Plasma Physics via Computer Simulation (Boca Raton: CRC Press
[47]	Huang C et al 2015 Geophys. Res. Lett. 42 7282 doi: 10.1002/2015GL065690
[48]	Lu Q M et al 2013 Plasma Phys. Control. Fusion 55 085019 doi: 10.1088/0741-3335/55/8/085019
[49]	Lu Q M et al 2010 J. Geophys. Res. Space Phys. 115 A11208
[50]	Egedal J et al 2010 Geophys. Res. Lett. 37 L10102
[51]	Hoshino M et al 2001 J. Geophys. Res. Space Phys. 106 25979 doi: 10.1029/2001JA900052
[52]	Pritchett P L 2001 J. Geophys. Res. Space Phys. 106 25961 doi: 10.1029/2001JA000016
[53]	Lapenta G et al 2010 Phys. Plasmas 17 082106 doi: 10.1063/1.3467503
[54]	Chang C et al 2022 Astrophys. J. 933 67 doi: 10.3847/1538-4357/ac738d
[55]	Huang C et al 2014 J. Geophys. Res. Space Phys. 119 6445 doi: 10.1002/2014JA019991
[56]	Goldman M V et al 2014 Phys. Rev. Lett. 112 145002 doi: 10.1103/PhysRevLett.112.145002
[57]	Wu M Y et al 2010 J. Geophys. Res. Space Phys. 115 A10245
[58]	Wu M Y et al 2010 Chin. Phys. Lett. 27 095201 doi: 10.1088/0256-307X/27/9/095201

[1]	Jianchao LI, Xiaoqing ZHANG, Yu ZHANG, Abba Alhaji BALA, Huiping LIU, Guohong ZHOU, Nengchao WANG, Da LI, Zhongyong CHEN, Zhoujun YANG, Zhipeng CHEN, Jiaolong DONG, Yonghua DING, the J-TEXT Team. Investigation of the J-TEXT plasma events by k-means clustering algorithm[J]. Plasma Science and Technology, 2023, 25(8): 085103. DOI: 10.1088/2058-6272/acc3d1
[2]	Qingdong ZENG, Guanghui CHEN, Wenxin LI, Zitao LI, Juhong TONG, Mengtian YUAN, Boyun WANG, Honghua MA, Yang LIU, Lianbo GUO, Huaqing YU. Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine[J]. Plasma Science and Technology, 2022, 24(8): 084009. DOI: 10.1088/2058-6272/ac72e3
[3]	Liuyang ZHAN (詹浏洋), Xiaohong MA (马晓红), Weiqi FANG (方玮骐), Rui WANG (王锐), Zesheng LIU (刘泽生), Yang SONG (宋阳), Huafeng ZHAO (赵华凤). A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm[J]. Plasma Science and Technology, 2019, 21(3): 34018-034018. DOI: 10.1088/2058-6272/aaf7bf
[4]	Chengxu LU (吕程序), Bo WANG (王博), Xunpeng JIANG (姜训鹏), Junning ZHANG (张俊宁), Kang NIU (牛康), Yanwei YUAN (苑严伟). Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks[J]. Plasma Science and Technology, 2019, 21(3): 34014-034014. DOI: 10.1088/2058-6272/aaef6e
[5]	Junwei JIA (贾军伟), Hongbo FU (付洪波), Zongyu HOU (侯宗余), Huadong WANG (王华东), Zhibo NI (倪志波), Fengzhong DONG (董凤忠). Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 2019, 21(3): 34003-034003. DOI: 10.1088/2058-6272/aae3e1
[6]	Yangmin GUO (郭阳敏), Yun TANG (唐云), Yu DU (杜宇), Shisong TANG (唐仕松), Lianbo GUO (郭连波), Xiangyou LI (李祥友), Yongfeng LU (陆永枫), Xiaoyan ZENG (曾晓雁). Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means[J]. Plasma Science and Technology, 2018, 20(6): 65505-065505. DOI: 10.1088/2058-6272/aaaade
[7]	Wei ZHANG (张伟), Tongyu WU (吴彤宇), Bowen ZHENG (郑博文), Shiping LI (李世平), Yipo ZHANG (张轶泼), Zejie YIN (阴泽杰). A real-time neutron-gamma discriminator based on the support vector machine method for the time-of-fiight neutron spectrometer[J]. Plasma Science and Technology, 2018, 20(4): 45601-045601. DOI: 10.1088/2058-6272/aaaaa9
[8]	Dingliang TANG (汤丁亮), Xianhui ZHANG (张先徽), Si-ze YANG (杨思泽). Plasma electrolytic liquefaction of cellulosic biomass[J]. Plasma Science and Technology, 2018, 20(4): 44002-044002. DOI: 10.1088/2058-6272/aa9563
[9]	JIN Shuo (金硕), RUAN Jiangjun (阮江军), DU Zhiye (杜志叶), ZHU Lin (朱琳), SHU Shengwen (舒胜文). Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine[J]. Plasma Science and Technology, 2016, 18(10): 998-1004. DOI: 10.1088/1009-0630/18/10/06
[10]	YANG Guang (杨光), QIAO Shujun (乔淑君), CHEN Pengfei (陈鹏飞), DING Yu (丁宇), TIAN Di (田地). Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library[J]. Plasma Science and Technology, 2015, 17(8): 656-663. DOI: 10.1088/1009-0630/17/8/08

Cited By

Cited by

Periodical cited type(25)

1.	Wang, D., Xu, L., Gao, W. et al. Application of Semi-Supervised Learning Model to Coal Sample Classification. Applied Sciences (Switzerland), 2024, 14(4): 1606. DOI:10.3390/app14041606
2.	Sun, Y., Liu, L., Xiao, L. Application of Wavelet Threshold Denoising in LIBS Spectral Denoising. 2024. DOI:10.1109/EIT63098.2024.10762555
3.	Wu, L., Kim, S.K. Evaluating the economic and climate adaptation benefits of land conservation strategies in urban coastal regions of the U.S. and China. Climate Risk Management, 2024. DOI:10.1016/j.crm.2024.100632
4.	Dong, M., Cai, J., Liu, H. et al. A review of laser-induced breakdown spectroscopy and spontaneous emission techniques in monitoring thermal conversion of fuels. Spectrochimica Acta - Part B Atomic Spectroscopy, 2023. DOI:10.1016/j.sab.2023.106807
5.	Yang, L., Xiang, Y., Li, Y. et al. Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology. AIP Advances, 2023, 13(7): 075024. DOI:10.1063/5.0149329
6.	Xu, L., Shu, Q., Fu, H. et al. Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data. Forests, 2023, 14(5): 876. DOI:10.3390/f14050876
7.	Brunnbauer, L., Gajarska, Z., Lohninger, H. et al. A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS). TrAC - Trends in Analytical Chemistry, 2023. DOI:10.1016/j.trac.2022.116859
8.	Guan, C., Wu, T., Chen, J. et al. Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method. Chemosensors, 2022, 10(11): 490. DOI:10.3390/chemosensors10110490
9.	Zhang, Q., Liu, Y. Review of In-situ Online LIBS Detection in the Atmospheric Environment. Atomic Spectroscopy, 2022, 43(2): 174-185. DOI:10.46770/AS.2021.609
10.	Zhang, D., Zhang, H., Zhao, Y. et al. A brief review of new data analysis methods of laser-induced breakdown spectroscopy: machine learning. Applied Spectroscopy Reviews, 2022, 57(2): 89-111. DOI:10.1080/05704928.2020.1843175
11.	Kim, S.K., Bennett, M.M., van Gevelt, T. et al. Urban agglomeration worsens spatial disparities in climate adaptation. Scientific Reports, 2021, 11(1): 8446. DOI:10.1038/s41598-021-87739-1
12.	Song, Y., Song, W., Yu, X. et al. Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra. Analytica Chimica Acta, 2021. DOI:10.1016/j.aca.2021.339053
13.	Liu, K., He, C., Zhu, C. et al. A review of laser-induced breakdown spectroscopy for coal analysis. TrAC - Trends in Analytical Chemistry, 2021. DOI:10.1016/j.trac.2021.116357
14.	Teng, G., Wang, Q., Cui, X. et al. Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis. Biomedical Optics Express, 2021, 12(7): 4438-4451. DOI:10.1364/BOE.431356
15.	Yang, Y., Zhang, L., Hao, X. et al. Classification of iron ore based on machine learning and laser induced breakdown spectroscopy \| [机器学习结合激光诱导击穿光谱技术铁矿石分类方法]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2021, 50(5): 20200490. DOI:10.3788/IRLA20200490
16.	Wang, C., Wang, J., Wang, J. et al. Classification of 13 original rock samples by laser induced breakdown spectroscopy. Laser Physics, 2021, 31(3): 035601. DOI:10.1088/1555-6611/abdfc8
17.	Jayaganthan, S., Babu, M.S., Vasa, N.J. et al. Classification of coal deposited epoxy micro-nanocomposites by adopting machine learning techniques to libs analysis. Journal of Physics Communications, 2021, 5(10): 105006. DOI:10.1088/2399-6528/ac2b5d
18.	Liu, X., Che, X., Li, K. et al. Geographical authenticity evaluation of Mentha haplocalyx by LIBS coupled with multivariate analyzes. Plasma Science and Technology, 2020, 22(7): 074006. DOI:10.1088/2058-6272/ab7eda
19.	Feng, Z., Zhang, D., Wang, B. et al. The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods. Plasma Science and Technology, 2020, 22(7): 074012. DOI:10.1088/2058-6272/ab84ed
20.	Dong, M., Wei, L., González, J.J. et al. Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry. Analytical Chemistry, 2020, 92(10): 7003-7010. DOI:10.1021/acs.analchem.0c00188
21.	Yang, Y., Hao, X., Zhang, L. et al. Application of scikit and keras libraries for the classification of iron ore data acquired by laser-induced breakdown spectroscopy (LIBS). Sensors (Switzerland), 2020, 20(5): 1393. DOI:10.3390/s20051393
22.	Wang, Z., Hou, Z., Zhang, L. et al. Coal analysis. Laser-Induced Breakdown Spectroscopy, Second Edition, 2020. DOI:10.1016/B978-0-12-818829-3.00021-6
23.	Xia, W., Zeng, J., Long, Z. et al. Study on fracture fault diagnosis online method of bogie of maglev train. 2019. DOI:10.1109/CAC48633.2019.8996297
24.	Wang, Z., Wang, S., Kong, D. et al. Methane detection based on improved chicken algorithm optimization support vector machine. Applied Sciences (Switzerland), 2019, 9(9): 1761. DOI:10.3390/app9091761
25.	Fu, Y., Hou, Z., Deguchi, Y. et al. From big to strong: Growth of the Asian laser-induced breakdown spectroscopy community. Plasma Science and Technology, 2019, 21(3): 030101. DOI:10.1088/2058-6272/aaf873

Other cited types(0)

Figures(4)

Get Citation

PDF

XML

Article views (19) PDF downloads (12) Cited by(25)

Turn off MathJax

Article Contents

Abstract

Acknowledgments

References

The influence of boundary conditions on the distribution of energetic electrons during collisionless magnetic reconnection

Abstract