2023/12/14 by Philip C. Myers, Myers, Philip C., Ian Stephens +3 · 1 citation
Physics and Astronomy · #Astrophysics and Star Formation Studies #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Ionosphere and magnetosphere dynamics #Solar and Space Plasma Dynamics
paper · pdf · doi:10.48550/arxiv.2312.09330
openalex publication_date 2023/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Davis-Chandrasekhar-Fermi (DCF) method is widely used to evaluate magnetic fields in star-forming regions. Yet it remains unclear how well DCF equations estimate the mean plane-of-the-sky field strength in a map region. To address this question, five DCF equations are applied to an idealized cloud map. Its polarization angles have a normal distribution with dispersion σθ,and its density and velocity dispersion have negligible variation. Each DCF equation specifies a global field strength BDCF and a distribution of local DCF estimates. The "most-likely" DCF field strength Bml is the distribution mode (Chen et al. 2022), for which a correction factor βml = Bml/BDCF is calculated analytically. For each equation βml < 1, indicating that BDCF is a biased estimator of Bml. The values of βml are βml≈ 0.7 when BDCF ∝ σθ-1 due to turbulent excitation of Afvénic MHD waves, and βml≈ 0.9 when BDCF ∝ σθ-1/2 due to non-Alfvénic MHD waves. These statistical correction factors βml have partial agreement with correction factors βsim obtained from MHD simulations. The relative importance of the statistical correction is estimated by assuming that each simulation correction has both a statistical and a physical component. Then the standard, structure function, and original DCF equations appear most accurate because they require the least physical correction. Their relative physical correction factors are 0.1, 0.3, and 0.4 on a scale from 0 to 1. In contrast the large-angle and parallel-δB equations have physical correction factors 0.6 and 0.7. These results may be useful in selecting DCF equations, within model limitations.