3.8 Creating PAH Spectra¶

The (theoretical) database XML-files provide fundamental vibrational transitions at 0-Kelvin. To compare these with observations, they need to be transformed into a spectral density, i.e., spectra. When not dealing with absorption at 0-Kelvin, an emission model is required as well.

3.8.1 Emission models¶

The software tools offer three PAH emission models. With increasing complexity they are the ‘fixedtemperature’, ‘calculatedtemperature’, and ‘cascade’ model. The first simply multiplies a blackbody at fixed given temperature with the integrated cross-section of each vibrational transition. The second first calculates the maximum attained temperature from the provided input and subsequently multiplies a blackbody at that fixed temperature with the integrated cross-section of each vibrational transition. The third averages the total emission over the entire cooling cascade (time).

Emission models are handled by the ‘transitions’-instance. The ‘FixedTemperature’-model simply takes a temperature, in Kelvin, and, in their simplest form, both the ‘calculatedtemperature’ and ‘cascade’ models take an energy, in erg.

transitions.fixedtemperature(600)

transitions.calculatedtemperature(4.0 * 1.603e-12)

transitions.cascade(6 * 1.603e-12)

Both the ‘calculatedtemperature’ and ‘cascade’-methods accept the ‘approximate’, ‘star’, ‘stellarmodel’, and ‘isrf’-keywords. With the ‘approximate’-keyword specified, calculations are performed using the PAH emission model from Bakes et al. (2001a, b). When the ‘star’-keyword is set, a stellar blackbody at the provided temperature is used to calculate the average energy absorbed by each PAH utilizing the PAH absorption cross-sections from Draine & Li (2007). In case the ‘stellarmodel’-keyword is provided as well, the input is considered to be a full-blown, for example, kurucz stellar atmosphere model. In the IDL case, the ‘AmesPAHdbIDLSuite_CREATE_KURUCZ_STELLARMODEL_S’ helper routine is provided to assist with molding the model data into the proper input format. Lastly, with the ‘isrf’-keyword set, the interstellar radiation field from Mathis et al. (1983) is used to calculate the average energy absorbed by each PAH.

transitions.calculatedtemperature(17000, star=True)

transitions.cascade(precision='approximate', field='isrf')

The ‘cascade’-method also accepts the ‘convolve’-keyword. When set and combined with either the ‘star’, optionally with the ‘stellarModel’-keyword, or ‘isrf’-keyword, will instead of calculating the average absorbed photon energy for each PAH, convolve the PAH emission with the entire radiation field.

transitions.cascade(17000, star=True, convolve=True)

NB This is computationally expensive.

The ‘transitions’-instance’s ‘shift’-method can be used to redshift the fundamental transitions to simulate anharmonic effects.

transitions.shift(-15)

NB Red-shifting the fundamental vibrational transitions should be done after applying one of the three emission models described above.

3.8.2 Line profiles¶

Line profiles are also handled by the ‘transitions’-intance and it provides three profiles Lorentzian, Gaussian and Drude. Convolution with the keyword chosen line profile is achieved through the ‘transitions’-instance’s ‘convolve’-method, which will return the convolved spectrum in the form of a ‘spectrum’-instance.

spectrum = transitions.convolve(drude=True)

Optionally, the ‘convolve’-method accepts the ‘fwhm’, ‘grid’, ‘npoints’, and ‘xrange’-keywords, which control the full-width-at-half-maximum of the selected line profile (in cm^-1), convolution onto a specified grid, the number of resolution elements in the generated spectrum, and the frequency range (in cm^-1) of the spectrum.

spectrum = transitions.convolve(drude=True, fwhm=20.0, grid=myGrid)

The ‘spectrum’-instance exposes convolved spectra and provides the ‘plot’, and ‘write’-methods. The ‘plot’-method will display the spectrum of each PAH species in a different color. The ‘write’-method will write all spectra to an IPAC table (.tbl). Optionally, a filename can be provided.

spectrum.plot()

spectrum.write('myFile')

Optionally, the ‘wavelength’, ‘stick’, ‘oplot’, ‘legend’, and ‘color’-keywords can be given to the ‘plot’-method to control abscissa, stick representation, overplotting, legend and color, respectively.

spectrum.plot(wavelength=True)