GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

Abstract 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 James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 0.010 − 0.001 + 0.003 , stellar mass with rms = 0.089 − 0.022 + 0.044 , halo mass with a mean-squared error of 0.022 − 0.008 + 0.014 , and mass-weighted age with rms = 12.466 − 2.408 + 5.065 . We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias 〈Δ z 〉 < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.