The Galaxy Assembly and Interaction Neural Networks (GAINN) for high-redshift JWST observations

Understand

We present the Galaxy Assembly and Interaction Neural Networks (GAINN), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on JWST photometry.

• Our goal is to determine the best neural network for predicting these variables at $11.5 < z < 15$.
• The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies.
• The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M).