Magnetic fields on FIRE: Comparing B-fields in the multiphase ISM and CGM of simulated L* galaxies to observations. Issue 3 (20th September 2022) - Ponnada, Sam B; Panopoulou, Georgia V; Butsky, Iryna S; Hopkins, Philip F; Loebman, Sarah R; Hummels, Cameron; Ji, Suoqing; Wetzel, Andrew; Faucher-Giguère, Claude-André; Hayward, Christopher C
ABSTRACT: The physics of magnetic fields (B ) and cosmic rays (CRs) have recently been included in simulations of galaxy formation. However, significant uncertainties remain in how these components affect galaxy evolution. To understand their common observational tracers, we analyse the magnetic fields in a set of high-resolution, magnetohydrodynamic, cosmological simulations of Milky-Way-like galaxies from the FIRE-2 project. We compare mock observables of magnetic field tracers for simulations with and without CRs to observations of Zeeman splitting and rotation/dispersion measures. We find reasonable agreement between simulations and observations in both the neutral and the ionized interstellar medium (ISM). We find that the simulated galaxies with CRs show weaker ISM |B | fields on average compared to their magnetic-field-only counterparts. This is a manifestation of the effects of CRs in the diffuse, low density inner circumgalactic medium (CGM). We find that equipartition between magnetic and cosmic ray energy densities may be valid at large (> 1 kpc) scales for typical ISM densities of Milky-Way-like galaxies, but not in their haloes. Within the ISM, the magnetic fields in our simulated galaxies follow a power-law scaling with gas density. The scaling extends down to neutral hydrogen number densities < 300 cm −3, in contrast to observationally derived models, but consistent with the observational measurements. Finally, we generate synthetic rotation measure (RM) profiles for projections of the simulated galaxies and compare to observational constraints in the CGM. While consistent with upper limits, improved data are needed to detect the predicted CGM RMs at 10–200 kpc and better constrain theoretical predictions.