Starduster: A multi-wavelength SED model based on radiative transfer simulations and deep learning

Understand

We present Starduster, a supervised deep learning model that predicts the multi-wavelength SED from galaxy geometry parameters and star formation history by emulating dust radiative transfer simulations.

• The model is comprised of three specifically designed neural networks, which take into account the features of dust attenuation and emission.
• We utilise the Skirt radiative transfer simulation to produce data for the training data of neural networks.
• Each neural network can be trained using ~ 4000 - 5000 samples.