Published November 2025 | Version v2

MAPPINGS V Ionization Models

  • 1. ROR icon University of Edinburgh

Contributors

  • 1. ROR icon University of Edinburgh

Description

MAPPINGS V v5.2.1 Ionization Models

File Names

File names are meant to be intuitive and indicate the ionization source with a
prefix 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 ionized
region.

Table Formats

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

The next few subsequent columns contain the unique assumptions regarding that
particular 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, we
recommend users join tables using pandas as in the following example for the
shock+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 following
example 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 of
these lines were considered for our inaugural publication, which focused on the
stronger FUV lines. These lines include emission from various species of the
following 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 Arabic
numeral, Wave is the wavelength, and U is the unit of the wavelength. If the
line is a recombination line, it will contain the ‘r’ suffix. For wavelengths
less than 1 um, we express Wave in A as a whole number. For wavelengths greater
than 1 um, we express Wave in um with two decimal places. Some
examples:

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 file
in 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.

Files

aa_line_list.md

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Additional details

Related works

Is supplement to
Journal article: arXiv:2412.06763 (arXiv)
Publication: 2025MNRAS.543.3367F (Bibcode)
Publication: 10.1093/mnras/staf1615 (DOI)

Dates

Submitted
2024-12-02
Accepted
2025-08-20
Issued
2025-10-28

References

  • Flury, S. R., Arellano-Córdova, K. Z., Moran, E. C., et al. 2025, MNRAS, 543, 4, 3367