The Built-In Libraries¶
Filters¶
MCSED comes with the transmission curves for \(\sim 150\) broad-
and narrow-band filters, including sets of filters from Hubble Space
Telescopes’s ACS, WFC3, and WFPC2 cameras, Spitzer’s IRAC and MIPS
detectors, Herschel’s PACS and SPIRE instruments, the Swift UVOT, GALEX,
Subaru’s Suprime-Cam and MOIRCS, the CFHT’s MegaCam and WIRCam, NOAO’s
NEWFIRM and Mosaic Cameras, Keck’s LRIS, the VLT’s VIMOS and ISAAC, the
Magellan Telescopes’s Four Star, and the Sloan Digital Sky Survey.
Additional filters can easily be added to MCSED’s library in the
FILTERS subdirectory.
Additional filters can easily be added to MCSED’s library in the FILTERS subdirectory by providing a fi le with two columns
containing the wavelength in Angstroms and the relative transmission.
Stellar Libraries¶
Because MCSED is modular, it is relatively straightforward to change
its stellar evolution libraries. MCSED is distributed with a grid of
SSP spectra generated from the Padova isochrones (Bressan et al. 2012)
by the Flexible Stellar Population Synthesis code developed by Conroy
et al. (2009) and Conroy & Gunn (2010). This library, which contains 22
metallicities ranging from \(-1.98\) to \(+0.20\) in
\(\log Z/Z_{\odot}\) and 84 ages ranging from 6 to 10.15 in log yr,
can be found in the directory SSP. MCSED builds complex stellar
populations (CSPs) from a linear combinations of these SSPs.
If a user wishes to use a different set of SSP spectra, they can simply
add a subroutine within ssp.py, which returns the following arrays
to run_mcsed_fit.py:
| Array | Dimen. | Length | Description | Units |
|---|---|---|---|---|
ages |
1 | [Ages] | SSP ages | Gyr |
wave |
1 | [Wavelengths] | Spectral Wavelengths | Å |
SSP |
3 | [Wavelengths, Ages, Metallicities] | Spectral Fluxes | \(\mu\)Jy \(M_{\odot}^{-1}\) at 10 pc |
met |
1 | [Metallicities] | SSP Metallicities | Z |
linewave |
1 | [Line wavelengths] | Emission Line Wavelengths | Å |
lineSSP |
3 | [Line wavelengths, Ages, Metallicities] | Emission Line Fluxes | ergs cm\(^{-2}\) s\(^{-1}\) at 10 pc |
This subroutine should be called as an alternative to
ages, masses, wave, SSP, met, linewave, lineSSP = read_ssp_fsps(args) in
run_mcsed_fit.py. The last two arrays in the table contain the
wavelengths and model line strengths for emission-lines that may be used
in the computation of the fit likelihood (see Nebular Libraries). The emission lines in this grid are drawn from the emline_list_dict
(defined in config.py) and will only include lines that also appear in the input file (i.e., those which will
be used in the model selection). Otherwise, these variables will not be used in the calculation and can be
arrays of arbitrary values (but must be of the appropriate dimensions).
Nebular Libraries¶
Nebular continuum and line emission can be important for star-forming
populations. MCSED is distributed with data generated by the
CLOUDY photo-ionization code (Ferland 1998, 2013), which have been
organized by Byler et al. (2017) into a grid with 11 metallicities
(\(-2.0 \leq \log Z/Z_{\odot} \leq +0.2\)), 7 ionization parameters
(\(-4 < \log U < -1\)), and 9 ages
(\(0.5 \leq t({\rm Myr}) < 10\)). One important feature of this grid
is its self-consistency with the Padova isochrone-FSPS spectra: the lines and continuum fluxes nebular line and continuum emission were generated using the
same SSP SEDs included in MCSED. The data files from Byler
et al. (2017), which separate continuum and line emission, are stored in
the subdirectory nebular, and are read in within ssp.py.
If the user wishes to employ a different grid of nebular emission, this
grid should be calculated in a self-consistent manner from the stellar
SSPs, and simply added onto the 3-D grid of stellar spectral flux
densities stored in the array SSP (see Stellar Libraries). In addition, to speed the analysis, the user should store the wavelengths and model monochromatic
fluxes for all emission lines that may be used in the maximum likelihood
calculation in the arrays linewave and lineSSP (see Emission Lines).