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FSPS is a flexible SPS package that allows the user to compute simple stellar populations (SSPs) for a range of IMFs and metallicities, and for a variety of assumptions regarding the morphology of the horizontal branch, the blue straggler population, the post--AGB phase, and the location in the HR diagram of the TP-AGB phase. From these SSPs the user may then generate composite stellar populations (CSPs) for a variety of star formation histories (SFHs) and dust attenuation prescriptions. Outputs include the "observed" spectra and magnitudes of the SSPs and CSPs at arbitrary redshift. In addition to these fortran routines, several IDL routines are provided that allow easy manipulation of the output. FSPS was designed with the intention that the user would make full use of the provided fortran routines. However, the full FSPS package is quite large, and requires some time for the user to become familiar with all of the options and syntax. Some users may only need SSPs for a range of metallicities and IMFs. For such users, standard SSP sets for several IMFs, evolutionary tracks, and spectral libraries are available here.
Prospector conducts principled inference of stellar population properties from photometric and/or spectroscopic data. The code combine photometric and spectroscopic data rigorously using a flexible spectroscopic calibration model and infer high-dimensional stellar population properties using parameteric SFHs (with ensemble MCMC sampling). Prospector also constrains the linear combination of stellar population components that are present in a galaxy (e.g. non-parametric SFHs) using spectra and/or photometry, and fits individual stellar spectra using large interpolated grids.
The dust radiative transfer software Powderday interfaces with galaxy formation simulations to produce spectral energy distributions and images. The code uses fsps (ascl:1010.043) and its Python bindings python-fsps for stellar SEDs, Hyperion (ascl:1207.004) for dust radiative transfer, and works with a variety of packages, including Arepo (ascl:1909.010), Changa (ascl:1105.005), Gasoline (ascl:1710.019), and Gizmo (ascl:1410.003); threaded throughout is yt (ascl:1011.022).
The UniverseMachine applies simple empirical models of galaxy formation to dark matter halo merger trees. For each model, it generates an entire mock universe, which it then observes in the same way as the real Universe to calculate a likelihood function. It includes an advanced MCMC algorithm to explore the allowed parameter space of empirical models that are consistent with observations.