Atmospheric models

In this tutorial, we will have a look at some spectra of the DRIFT-PHOENIX atmospheric model. The spectra are first downloaded and added to the database. Then we will use the functionalities of ReadModel to extract a spectrum and calculate a photometric flux.

Getting started

We start by importing the required Python modules.

import species
from IPython.display import Image

Then we initialize species with SpeciesInit, which creates a default configuration file and the HDF5 database.

Initiating species v0.4.0... [DONE]
Creating species_config.ini... [DONE]
Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5
Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data
Working folder: /Users/tomasstolker/applications/species/docs/tutorials
Creating species_database.hdf5... [DONE]
Creating data folder... [DONE]
<species.core.init.SpeciesInit at 0x14f0cae80>

Adding model spectra to the database

To store the spectra, we first create an instance of Database.

database = species.Database()

Next, we will import the model spectra with the add_model method of Database. This step will automatically download the DRIFT-PHOENIX spectra (R = 2000) to the data_folder. The dowloaded data will then be unpacked and added to the database. We restrict the temperature range to 1300-1700 K, so not all spectra are added to the databse.

database.add_model(model='drift-phoenix', teff_range=(1300., 1700.))
Downloading DRIFT-PHOENIX model spectra (229 MB)... [DONE]
Unpacking DRIFT-PHOENIX model spectra (229 MB)... [DONE]
Adding DRIFT-PHOENIX model spectra... [DONE]
Grid points stored in the database:
   - Teff = [1300. 1400. 1500. 1600. 1700.]
   - log(g) = [3.  3.5 4.  4.5 5.  5.5]
   - [Fe/H] = [-0.6 -0.3 -0.   0.3]
Number of grid points per parameter:
   - teff: 5
   - logg: 6
   - feh: 4
/Users/tomasstolker/applications/species/species/util/ RuntimeWarning: divide by zero encountered in log10
  flux = np.log10(flux)
Fix missing grid points with a linear interpolation:
   - teff = 1600.0, logg = 3.0, feh = 0.3
   - teff = 1600.0, logg = 5.5, feh = 0.3
Number of stored grid points: 120
Number of interpolated grid points: 2
Number of missing grid points: 0

Two of the grid points were missing in the original data and have been added with a linear, multidimensional interpolation.

Interpolating the model grid

We will read the spectra from the database by creating an instance of ReadModel.

read_model = species.ReadModel(model='drift-phoenix', wavel_range=(0.5, 10.))

Let’s see what the grid boundaries are from the spectra that are stored in the database.

{'teff': (1300.0, 1700.0), 'logg': (3.0, 5.5), 'feh': (-0.6, 0.3)}

We will now interpolate the grid in the (Teff, log(g), [Fe/H]) space at some specific parameter values, which need to be provided in a dictionary. The radius and distance are optional, otherwise the emitted flux is given at the planet surface.

model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100.}

To interpolate a spectrum, we use the get_model method and provide the parameter dictionary, and also an optional spectral resolution. Together with smooth=True, the spectrum will be smoothed (but not resampeld) to the given spectral resolution.

modelbox = read_model.get_model(model_param=model_param, spec_res=200., smooth=True)

The data is stored in a ModelBox. Let’s have a look at its content.

Opening ModelBox...
model = drift-phoenix
type = None
wavelength = [ 0.49989727  0.50002105  0.50014486 ...  9.99710369  9.99957902
flux = [9.44339097e-20 9.38011967e-20 9.31147817e-20 ... 1.53571943e-18
 1.53924349e-18 1.54301688e-18]
parameters = {'teff': 1510.0, 'logg': 4.1, 'feh': 0.1, 'radius': 1.0, 'distance': 100.0, 'luminosity': 4.729862212008143e-05, 'mass': 4.857062223118246}
quantity = flux
contribution = None

We will now use the same atmospheric parameters but we will add some ISM extinction with the relation from Cardelli et al. (1989). Therefore, we add the V band extinction and reddening parameters to the dictionary.

model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100., 'ism_ext': 5., 'ism_red': 3.}

We use again the get_model method and store the result in a different ModelBox.

model_ext = read_model.get_model(model_param=model_param, spec_res=200., smooth=True)

The two boxes with the model spectra are provided to the plot_spectrum. We also include some filter profiles to indicate where the telluric windows are.

species.plot_spectrum(boxes=[modelbox, model_ext],
                      filters=['MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.K', 'MKO/NSFCam.Lp', 'MKO/NSFCam.Mp'],
                      offset=(-0.08, -0.04),
                      xlim=(0.8, 5.),
                      ylim=(0., 5.5e-17),
                      legend={'loc': 'lower right', 'frameon': False, 'fontsize': 12.},
Adding filter: MKO/NSFCam.J... [DONE]
Adding filter: MKO/NSFCam.H... [DONE]
Adding filter: MKO/NSFCam.K... [DONE]
Adding filter: MKO/NSFCam.Lp... [DONE]
Adding filter: MKO/NSFCam.Mp... [DONE]
Plotting spectrum: model_spectrum.png... [DONE]

Let’s have a look at the result!


Extracting a spectrum at a grid point

It is also possible to extract a spectrum at one of the grid points, which doesn’t require any interpolation. Let’s first check with the get_points method what parameter values are stored in the database.

{'teff': array([1300., 1400., 1500., 1600., 1700.]),
 'logg': array([3. , 3.5, 4. , 4.5, 5. , 5.5]),
 'feh': array([-0.6, -0.3, -0. ,  0.3])}

We create a dictionary with values at one of the grid points.

model_param = {'teff':1500., 'logg':4., 'feh':0.}

And now use the get_data method to extract a spectrum.

modelbox = read_model.get_data(model_param)

Let’s make another plot with plot_spectrum.

                      offset=(-0.1, -0.05),
                      xlim=(0.8, 5.),
                      ylim=(0., 1e5),
                      legend={'loc': 'upper right', 'frameon': False, 'fontsize': 12.},
                      figsize=(8., 3.),
Plotting spectrum: model_spectrum.png... [DONE]

The spectrum is now shown at the spectral resolution as stored in the database (R = 2000).


Calculating synthetic photometry

The ReadModel class can also be used for calculating photometric fluxes and magnitudes. To do so, we create a new instance and set the filter_name argument to the VLT/NACO M’ filter. This will automatically downloadd and addd the filter profile.

read_model = species.ReadModel(model='drift-phoenix', filter_name='Paranal/NACO.Mp')
Adding filter: Paranal/NACO.Mp... [DONE]

We create again a dictionary with the parameters but now run the get_flux method, which returns the flux in W m-2 um-1.

model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100.}
flux = read_model.get_flux(model_param)
print(f'Flux (W m-2 um-1) = {flux[0]:.2e}')
Flux (W m-2 um-1) = 1.33e-17

Since we provided a radius and distance, the emitted flux at the planet surface has been scaled by (radius/distance):math:^2.

Similarly, we can use the get_magnitude method to calculate the magnitude for the NACO M’ filter. Note that the returned absolute magnitude is set to None if the parameter dictionary does not contain a radius and distance.

app_mag, abs_mag = read_model.get_magnitude(model_param)
print(f'Apparent magnitude = {app_mag:.2f}')
print(f'Absolute magnitude = {abs_mag:.2f}')
Downloading Vega spectrum (270 kB)... [DONE]
Adding Vega spectrum... [DONE]
Apparent magnitude = 15.53
Absolute magnitude = 10.53

As expected, at a distance of 100 pc, the difference between the apparent and absolute magnitude is 5.