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[ascl:2202.023] Starduster: Radiative transfer and deep learning multi-wavelength SED model

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.

Code site:
https://github.com/yqiuu/starduster
Described in:
https://ui.adsabs.harvard.edu/abs/2021arXiv211214434Q
Bibcode:
2022ascl.soft02023Q

Views: 1975

ascl:2202.023
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