IMAGINE and GalMag integration

This package allows one to use magnetic fields produced by GalMag within the IMAGINE pipeline.

Installation

1. Install GalMag and IMAGINE.
2. Clone this repository or download the desired release
3. Install it as a regular python package, running:

cd PATH_TO_IMAGINE-GALMAG
pip install -e .

Usage

Basic Field usage

Each GalMag field component (halo/disk) is imported as a separate IMAGINE field.

from galmag_imagine import GalMagHaloField, GalMagDiskField 

This can be instantiated in the usual way, by providing a grid and a parameters dictionary.

halo_field = GalMagHaloField(grid=grid, 
                             parameters={'halo_radius': 15*u.kpc,
                                         'halo_ref_Bphi': 1*u.microgauss,
                                         'halo_rotation_characteristic_radius': 3*u.kpc,
                                         'halo_rotation_characteristic_height': 10*u.kpc,
                                         'halo_rotation_normalization': 220*u.km/u.s,
                                         'halo_turbulent_diffusivity': 5e27*u.cm*u.cm/u.s,
                                         'halo_alpha_effect': 1*u.km/u.s}, 
                             halo_symmetric_field=False, 
                             keep_galmag_field=True)
disk_field = GalMagDiskField(grid=grid, 
                             parameters={'mode_1': 4*u.microgauss,
                                         'mode_2': 2*u.microgauss, 
                                         'mode_4': .3*u.microgauss,
                                         'disk_height': 400*u.pc,
                                         'disk_radius': 17*u.kpc,
                                         'disk_alpha_effect': 1*u.km/u.s,
                                         'disk_shear_normalization': -35.36*u.km/u.s/u.kpc,
                                         'disk_turbulent_diffusivity': 5e25*u.cm*u.cm/u.s}, 
                             keep_galmag_field=True)  

These fields can then be provided to an IMAGINE Simulator.

Accessing the original GalMag field

If the optional keyword argument keep_galmag_field is set to True, the GalMag field component object is saved to the attribute galmag after the field is evaluated. Below we exemplify how to use the GalMag associated object to get the φ component of the magnetic field.

# Evaluates the field
disk_field.get_data()
# Gets the Bphi component
Bphi = disk_field.galmag.phi

Parameters

Changes in parametrization

There are some key differences between the way the models are parametrised in the native GalMag contex and in IMAGINE-GalMag Field object.

In the original GalMag parametrization, the amplitude of the modes for the disk field were specified using an array, while here (as it can be seen in the above example), each mode is treated as a separate parameter, where the name of the n-th mode is simply mode_n.

Aiming easier interpretation and cleaner prior specifications, the dimensionless dynamo parameters disk_dynamo_number, disk_turbulent_induction, halo_rotation_induction and halo_turbulent_induction were replaced by directly observable dimensional counterparts, namely:

• halo_alpha_effect, the normalization of the alpha profile for the halo at the reference position (by default, the Sun's position).
• halo_turbulent_diffusivity - turbulent magnetic diffusivity in the halo (assumed constant)
• halo_rotation_normalization - normalization of the halo circular velocity profile at infinity
• disk_alpha_effect - normalization of the disk alpha effect at the solar radius
• disk_shear_normalization - the shearing rate of the disk at the solar radius
• disk_turbulent_diffusivity - turbulent magnetic diffusivity in the disk (assumed constant)

Note that all dimensional parameters are specified with explicit units, to avoid any unit convertion mistakes.

Parameters which became keyword arguments

Some of GalMag parameters are not treated as IMAGINE parameters, as they are understood as part of the model settings, and thus are not meant to varied during an exploration of the parameter space.

Particularly important cases of this are the GalMag parameters which set the functional form of the rotation curve, disk scale-height or alpha-effect profile. In the example below we create a disk field based on a smooth exponential rotation curve (instead of the default case, which is based on the fit obtained by Clemens 1985):

from galmag.disk_profiles import simple_rotation_curve, simple_shear_rate

disk_field = GalMagDiskField(grid=grid, 
                             parameters={'mode_1': 4*u.microgauss,
                                         'mode_2': 2*u.microgauss, 
                                         'mode_4': .3*u.microgauss,
                                         'disk_height': 400*u.pc,
                                         'disk_radius': 17*u.kpc,
                                         'disk_alpha_effect': 1*u.km/u.s,
                                         'disk_shear_normalization': -35.36*u.km/u.s/u.kpc,
                                         'disk_turbulent_diffusivity': 5e25*u.cm*u.cm/u.s}, 
                             disk_rotation_function=simple_rotation_curve,
                             disk_shear_function=simple_shear_rate,
                             keep_galmag_field=True)