DOI: 10.5281/zenodo.14140948
title: MAPPINGS V Ionization Models

description: MAPPINGS V v5.2.1 Ionization Models

File Names

File names are meant to be intuitive and indicate the ionization source with aprefix and the contents with a suffix. A full list of prefixes is provided here.




Model
Prefix


AGN Jin+ 2012
agn-jin12


AGN OXAF isobaric bubble
agn-oxaf-cpr


AGN OXAF isochoric bubble
agn-oxaf-cdn


BPASS isobaric bubble
bpass-cpr-shl


BPASS isobaric filled sphere
bpass-cpr-sph


BPASS isobaric plane
bpass-cpr-ppl


BPASS isochoric bubble
bpass-cdn-shl


BPASS isochoric plane
bpass-cdn-ppl


STARBURST99 isobaric bubble
sb99-cpr-shl


STARBURST99 isochoric bubble
sb99-cdn-shl


shocks
shck


precursor
prec


shock + precursor
shckprec


dusty shocks
shck-dst


dusty precursor
prec-dst


dusty shock + precursor
shckprec-dst




Files ending in _fluxes.csv contain the line fluxes relative to Hβ.Files ending in _propts.csv contain nebular properties of the model ionizedregion.

Table Formats

Formatting is by comma-separated values. The first column will always be themodel identifier, or key. This key is specific to a row in both the flux andproperties tables of the same prefix. Tables are sorted by key identifier, whichcan be used to ensure that fluxes and properties are properly matched.

The next few subsequent columns contain the unique assumptions regarding thatparticular model, including ionization parameter, metallicity, velocity,density, magnetic field strength, and ionizing SED.

Future work will enable use of keys to join tables via SQL. Presently, werecommend users join tables using pandas as in the following example for theshock+precursor models, eliminating any duplicate column headings.

>> import pandas as pd
>> fluxes = pd.read_csv('shock-precursor_fluxes.csv')
>> propts = pd.read_csv('shock-precursor_propts.csv)
>> joiner = [k for k in fluxes.keys() if k in propts.keys()]
>> shkprc = fluxes.merge(propts, how='inner', on=joiner)


Multiple model sets can also be combined using pandas as in the followingexample for BPASS isobaric and isochoric models:

>> import pandas as pd
>> bpass_cpr = pd.read_csv('bpass-cpr_fluxes.csv')
>> bpass_cdn = pd.read_csv('bpass-cdn_fluxes.csv')
>> bpass_tot = pd.concat([bpass_cpr,bpass_cdn])
>> bpass_tot.reset_index(inplace=True)


Lines Included

We include a large suite of lines spanning FUV through the MIR. Only a subset ofthese lines were considered for our inaugural publication, which focused on thestronger FUV lines. These lines include emission from various species of thefollowing elements:H, He, C, N, O, S, Ne, Ar, Si, Mg, Ca, and Fe.

Lines are labeled following PyNeb formatting:

EI_WaveU

where E is the element (e.g., O for oxygen), I is the ion species as an Arabicnumeral, Wave is the wavelength, and U is the unit of the wavelength. If theline is a recombination line, it will contain the ‘r’ suffix. For wavelengthsless than 1 um, we express Wave in A as a whole number. For wavelengths greaterthan 1 um, we express Wave in um with two decimal places. Someexamples:




Emission Line
Column




C III] 1909 Å
C3_1909A


He II  4686 Å
He2r_4686A


[Ar II] 6.98 µm
Ar2_0698um


[O IV] 24.89 µm
O4_2489um




The full set of lines in this release is included in the aa_line_list.md filein this repository. If a line of interest is not included, please let us know,and we will add it as soon as we are able.

Properties Included

Total Hβ flux, metallicity as $\zeta_{\rm O}$, and gas density $\rm n_H$.For photoionization models, ionization parameter and assumed luminosity($Q(\rm H)$ for stellar populations, $L_{tot}$ for AGN)For shock models, shock velocity and magnetic field.

Total and dust-depleted abundances as 12+log(X/H)

Relative ionic abundances as $\chi_i/\chi$

Ionic temperatures and densities in units of K and cm$^{-3}$,respectively.