The connection between supernova remnants and the Galactic magnetic field: A global radio study of the axisymmetric sample

¹ Dept of Physics and Astronomy, University of Manitoba, Winnipeg, R3T 2N2, Canada
e-mail: jennifer.west@umanitoba.ca; samar.safi-harb@umanitoba.ca
² Université de Toulouse, UPS-OMP, IRAP, 31028 Toulouse, France
³ CNRS, IRAP, 9 Av. colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France
⁴ National Research Council, Herzberg Programs in Astronomy & Astrophysics, Dominion Radio Astrophysical Observatory, PO Box 248, Penticton, V2A 6J9, Canada
⁵ Dept of Physics & Astronomy, Brandon University, Brandon R7A 6A9, Canada

Received: 20 July 2015
Accepted: 28 October 2015

Abstract

The study of supernova remnants (SNRs) is fundamental to understanding the chemical enrichment and magnetism in galaxies, including our own Milky Way. In an effort to understand the connection between the morphology of SNRs and the Galactic magnetic field (GMF), we have examined the radio images of all known SNRs in our Galaxy and compiled a large sample that have an axisymmetric morphology, which we define to mean SNRs with a bilateral or barrel-shaped morphology, in addition to one-sided shells. We selected the cleanest examples and model each of these at their appropriate Galactic position using two GMF models, one of which includes a vertical halo component, and another that is oriented entirely parallel to the plane. Since the magnitude and relative orientation of the magnetic field changes with distance from the sun, we analyze a range of distances, from 0.5 to 10 kpc in each case. Using a physically motivated model of an SNR expanding into an ambient GMF that includes a vertical halo component, we find it is able to reproduce observed morphologies of many SNRs in our sample. These results strongly support the presence of an off-plane, vertical component to the GMF, and the importance of the Galactic field on SNR morphology. Our approach also provides a potentially new method for determining distances to SNRs, or conversely, distances to features in the large-scale GMF if SNR distances are known.