A method for reconstructing the variance of a 3D physical field from 2D observations: application to turbulence in the interstellar medium

被引:88
|
作者
Brunt, C. M. [1 ]
Federrath, C. [2 ,3 ]
Price, D. J. [4 ]
机构
[1] Univ Exeter, Sch Phys, Exeter EX4 4QL, Devon, England
[2] Heidelberg Univ, Zentrum Astron, Inst Theoret Astrophys, D-69120 Heidelberg, Germany
[3] Max Planck Inst Astron, D-69117 Heidelberg, Germany
[4] Monash Univ, Sch Math Sci, Ctr Stellar & Planetary Astrophys, Clayton, Vic 3168, Australia
关键词
MHD; turbulence; methods: statistical; ISM: clouds; ISM: kinematics and dynamics; ISM: structure; INITIAL MASS FUNCTION; STAR-FORMATION; MAGNETOHYDRODYNAMIC TURBULENCE; PROBABILITY-DISTRIBUTION; COLUMN DENSITY; VELOCITY;
D O I
10.1111/j.1365-2966.2009.16215.x
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We introduce and test an expression for calculating the variance of a physical field in three dimensions using only information contained in the two-dimensional projection of the field. The method is general but assumes statistical isotropy. To test the method we apply it to numerical simulations of hydrodynamic and magnetohydrodynamic turbulence in molecular clouds, and demonstrate that it can recover the three-dimensional (3D) normalized density variance with similar to 10 per cent accuracy if the assumption of isotropy is valid. We show that the assumption of isotropy breaks down at low sonic Mach number if the turbulence is sub-Alfvenic. Theoretical predictions suggest that the 3D density variance should increase proportionally to the square of the Mach number of the turbulence. Application of our method will allow this prediction to be tested observationally and therefore constrain a large body of analytic models of star formation that rely on it.
引用
收藏
页码:1507 / 1515
页数:9
相关论文
共 50 条
  • [21] Adaptive face modelling for reconstructing 3D face shapes from single 2D images
    Maghari, Ashraf
    Venkat, Ibrahim
    Liao, Iman Yi
    Belaton, Bahari
    IET COMPUTER VISION, 2014, 8 (05) : 441 - 454
  • [22] Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images
    Yang, Yang
    Han, Junwei
    Zhang, Dingwen
    Cheng, De
    PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2021, PT II, 2021, 13020 : 153 - 166
  • [23] A systematic approach towards reconstructing 3D curved models from multiple 2D views
    Kuo, MH
    GRAPHICS RECOGNITION: ALGORITHMS AND SYSTEMS, 1998, 1389 : 265 - 279
  • [24] A "LEARN 2D, APPLY 3D" METHOD FOR 3D DECONVOLUTION MICROSCOPY
    Soulez, Ferreol
    2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014, : 1075 - 1078
  • [25] Decoding the third dimension in the metaverse: A comprehensive method for reconstructing 2D NFT portraits into 3D models
    Deng, Erqiang
    You, Li
    Khan, Fazlullah
    Zhu, Guosong
    Qin, Zhen
    Kumari, Saru
    Xiong, Hu
    Alturki, Ryan
    APPLIED SOFT COMPUTING, 2024, 165
  • [26] Design in 2D, model in 3D: Live 3D pose generation from 2D sketches
    Tosco, Paolo
    Mackey, Mark
    Cheeseright, Tim
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 258
  • [27] 2D or 3D?
    Mills, R
    COMPUTER-AIDED ENGINEERING, 1996, 15 (08): : 4 - 4
  • [28] An Improved Method for Building A 3D Model from 2D DICOM
    Van Sinh Nguyen
    Manh Ha Tran
    Hoang Minh Quang Vu
    2018 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND APPLICATIONS (ACOMP), 2018, : 125 - 131
  • [29] An open environment for reconstructing 2D images into 3D finite element models
    Hoppner, JM
    Throne, RD
    Olson, LG
    Windle, JR
    COMPUTERS IN CARDIOLOGY 1996, 1996, : 429 - 432
  • [30] On a general filter regularization method for the 2D and 3D Poisson equation in physical geodesy
    Nguyen Huy Tuan
    Binh Thanh Tran
    Le Dinh Long
    Advances in Difference Equations, 2014