model_galaxies.rst

Making model spectra: model_galaxy

Model galaxy spectra and associated observables are created using the model_galaxy class. Check out the first iPython notebook example for a quick-start guide to making models.

bagpipes.model_galaxy

The model_components dictionary

The first and most important argument passed to model_galaxy is the model_components dictionary. This contains all of the physical information about the model you wish to create. A complete guide to the model_components dictionary is provided here <model-components>.

Getting observables - photometry

In order to obtain predictions for photometric observations of a galaxy with the physical parameters defined in model_components it is necessary to define a list of filter curves through which observed fluxes should be calculated. This is done by defining a filt_list.

This is simply a list of paths (absolute or from the directory in which the code is being run) to the locations at which these filter curves are stored. The filter curve files should contain wavelengths in Angstroms in their first column and relative transmission values in their second.

Let's look at a simple example of some code that creates predictions for photometry through a series of filter curves. For this to work you'd first need to put the filter curve files in the correct location. For sourcing filter curves I recommed the SVO Filter Profile Service.

import bagpipes as pipes
import numpy as np

uvista_filt_list = ["uvista/CFHT_u.txt",
                        "uvista/CFHT_g.txt",
                        "uvista/CFHT_r.txt",
                        "uvista/CFHT_i+i2.txt",
                        "uvista/CFHT_z.txt",
                        "uvista/subaru_z",
                        "uvista/VISTA_Y.txt",
                        "uvista/VISTA_J.txt",
                        "uvista/VISTA_H.txt",
                        "uvista/VISTA_Ks.txt",
                        "uvista/IRAC1",
                        "uvista/IRAC2"]

model = pipes.model_galaxy(model_components, filt_list=uvista_filt_list)

model.plot()

We now have a Bagpipes model galaxy! The final command generates a plot of the predicted fluxes.

Photometry is accessible as model.photometry, which is a 1D array of flux values in erg/s/cm^2/A in the same order as the filter curves are specified in filt_list. The output flux units can be converted to microJanskys using the model_galaxy keyword argument phot_units="mujy".

Getting observables - spectroscopy

The process for obtaining model spectroscopy is simpler, just pass an array containing the desired wavelength sampling in Angstroms as the spec_wavs keyword argument.

import bagpipes as pipes
import numpy as np

obs_wavs = np.arange(2500., 7500., 5.)

model = pipes.model_galaxy(model_components, spec_wavs=obs_wavs)

model.plot()

The output spectrum is stored as model.spectrum which is a two column array, containing wavelengths in Angstroms and spectral fluxes in erg/s/cm^2/A by default. The output flux units can be converted to microJanskys using the model_galaxy keyword argument spec_units="mujy".

Getting observables - line fluxes

Emission line fluxes are stored in the model_galaxy.line_fluxes dictionary. The list of emission features is here. These are only non-zero if a nebular component is added to model_components.

Emission line naming conventions are the same as in Cloudy. The names in the above file are the keys for the lines in model_galaxy.line_fluxes. For example, the Lyman alpha flux is under:

model.line_fluxes["H  1  1215.67A"]

Emission line fluxes are returned in units of erg/s/cm^2.

Note on units at redshift zero

The units specified above apply at non-zero redshift, however at redshift zero the luminosity distance is zero which would lead to a division by zero error. At redshift zero the code instead returns luminosities, in erg/s/A for spectroscopy and photometry, and erg/s for emission lines.

Updating models

Creating a new model_galaxy is relatively slow, however changing parameter values in model_components and calling the update method of model_galaxy rapidly updates the output predictions described above.

It should be noted that the update method is designed to deal with changing numerical parameter values, not with adding or removing components of the model or changing non-numerical values such as the dust attenuation type.