Catalog-free modeling of galaxy types in deep images: Massive dimensional reduction with neural networks

Understand

Current models of galaxy evolution are constrained by the analysis of catalogs containing the flux and size of galaxies extracted from multiband deep fields carrying inevitable observational and extraction-related biases which can be highly correlated.

• In practice, taking all of these effects simultaneously into account is difficult, and derived models are inevitably biased.
• To address this issue, we use robust likelihood-free methods for the inference of luminosity function parameters, made possible via massive compression of multiband images using artificial neural networks.
• This technique makes the use of catalogs unnecessary when comparing observed and simulated multiband deep fields and constraining model parameters.