pPXF-SEW implements a full‑spectrum fitting method that combines the penalized pixel‑fitting (pPXF, ascl:1210.002) technique with an “Equivalent Widths Spectrum” (SEW) approach. It derives stellar and gas kinematics via pPXF and extracts stellar‑population parameters and dust attenuation without assuming a prior attenuation curve. The code processes observed spectra and outputs fits to both continuum and line features, incorporating the SEW method’s linear treatment of equivalent widths to handle multiple galaxy components. pPXF-SEW can be applied to galaxy and stellar datasets for joint kinematic and population synthesis.

The deep learning model Starduster emulates dust radiative transfer simulations, which significantly accelerates the computation of dust attenuation and emission. Starduster contains two specific generative models, which explicitly take into account the features of the dust attenuation curves and dust emission spectra. Both generative models should be trained by a set of characteristic outputs of a radiative transfer simulation. The obtained neural networks can produce realistic galaxy spectral energy distributions that satisfy the energy balance condition of dust attenuation and emission. Applications of Starduster include SED-fitting and SED-modeling from semi-analytic models.