A maximum entropy principle for inferring the distribution of 3D plasmoids
The principle of maximum entropy, a powerful and general method for inferring the distribution function given a set of constraints, is applied to deduce the overall distribution of 3D plasmoids (flux ropes/tubes) for systems where resistive MHD is applicable and large numbers of plasmoids are produced. The analysis is undertaken for the 3D case, with mass, total flux, and velocity serving as the variables of interest, on account of their physical and observational relevance. The distribution functions for the mass, width, total flux, and helicity exhibit a powerlaw behavior with exponents of 4/3, 2, 3, and 2, respectively, for small values, whilst all of them display an exponential falloff for large values. In contrast, the velocity distribution, as a function of v=v, is shown to be flat for v→0, and becomes a power law with an exponent of 7/3 for v→∞. Most of these results are nearly independent of the free parameters involved in this specific problem. In conclusion, a preliminary comparison of our results with the observational evidence is presented, and some of the ensuing space and astrophysical implications are briefly discussed.
 Authors:

^{[1]};
^{[2]}
 HarvardSmithsonian Center for Astrophysics, Cambridge, MA (United States); Harvard Univ., Cambridge, MA (United States). John A. Paulson School of Engineering and Applied Sciences
 Princeton Univ., NJ (United States). Dept. of Astrophysical Sciences; Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
 Publication Date:
 Grant/Contract Number:
 AGS1338944; AGS1552142; AC0209CH11466
 Type:
 Accepted Manuscript
 Journal Name:
 Physics of Plasmas
 Additional Journal Information:
 Journal Volume: 25; Journal Issue: 1; Journal ID: ISSN 1070664X
 Publisher:
 American Institute of Physics (AIP)
 Research Org:
 Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
 Sponsoring Org:
 USDOE; National Science Foundation (NSF)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 70 PLASMA PHYSICS AND FUSION TECHNOLOGY
 OSTI Identifier:
 1432661
 Alternate Identifier(s):
 OSTI ID: 1417426
Lingam, Manasvi, and Comisso, Luca. A maximum entropy principle for inferring the distribution of 3D plasmoids. United States: N. p.,
Web. doi:10.1063/1.5020887.
Lingam, Manasvi, & Comisso, Luca. A maximum entropy principle for inferring the distribution of 3D plasmoids. United States. doi:10.1063/1.5020887.
Lingam, Manasvi, and Comisso, Luca. 2018.
"A maximum entropy principle for inferring the distribution of 3D plasmoids". United States.
doi:10.1063/1.5020887.
@article{osti_1432661,
title = {A maximum entropy principle for inferring the distribution of 3D plasmoids},
author = {Lingam, Manasvi and Comisso, Luca},
abstractNote = {The principle of maximum entropy, a powerful and general method for inferring the distribution function given a set of constraints, is applied to deduce the overall distribution of 3D plasmoids (flux ropes/tubes) for systems where resistive MHD is applicable and large numbers of plasmoids are produced. The analysis is undertaken for the 3D case, with mass, total flux, and velocity serving as the variables of interest, on account of their physical and observational relevance. The distribution functions for the mass, width, total flux, and helicity exhibit a powerlaw behavior with exponents of 4/3, 2, 3, and 2, respectively, for small values, whilst all of them display an exponential falloff for large values. In contrast, the velocity distribution, as a function of v=v, is shown to be flat for v→0, and becomes a power law with an exponent of 7/3 for v→∞. Most of these results are nearly independent of the free parameters involved in this specific problem. In conclusion, a preliminary comparison of our results with the observational evidence is presented, and some of the ensuing space and astrophysical implications are briefly discussed.},
doi = {10.1063/1.5020887},
journal = {Physics of Plasmas},
number = 1,
volume = 25,
place = {United States},
year = {2018},
month = {1}
}