Improving the Accuracy of Magnetic Field Tracing by Velocity Gradients: Principal Component Analysis

TL;DR

This paper enhances magnetic field tracing accuracy using Velocity Gradient Technique by applying Principal Component Analysis to spectroscopic data, improving alignment with dust polarization in both simulated and real observations.

Contribution

It introduces PCA as a filtering method to improve VGT magnetic field tracing, validated with simulations and real 21 cm GALFA data.

Findings

PCA filtering improves magnetic field tracing accuracy.

Enhanced alignment between velocity gradients and dust polarization.

Effective in both subsonic and supersonic regimes.

Abstract

Tracing of the magnetic field with Velocity Gradient Technique (VGT) allows observers to probe magnetic field directions with spectroscopic data. In this paper, we employ the method of Principal Component Analysis (PCA) to extract the spectroscopic information most valuable for VGT. By using synthetic observation data from numerical simulations, we show that PCA acts in a way similar to spatial filtering along the velocity axis. We study both subsonic and supersonic simulations and show that with the PCA filtering the tracing of magnetic fields by the VGT is significantly improved. Using 21 cm GALFA data, we demonstrate that the PCA filtering improves the alignment of the velocity gradients and the Planck dust polarization.