Simulating Deep Hubble Images With Semi-empirical Models of Galaxy Formation

TL;DR

This paper presents a method to generate realistic deep Hubble images using semi-empirical galaxy formation models, enabling unbiased comparisons with observations by incorporating observational effects and selection biases.

Contribution

It introduces a forward modeling approach that simulates HST images from galaxy formation models, allowing direct comparison with real data while accounting for observational biases.

Findings

Simulated galaxy properties match observed distributions for reasonable model parameters.

Magnitude and size measurements are biased near detection limits.

Detection fractions depend on model parameters and observational effects.

Abstract

We simulate deep images from the Hubble Space Telescope (HST) using semi-empirical models of galaxy formation with only a few basic assumptions and parameters. We project our simulations all the way to the observational domain, adding cosmological and instrumental effects to the images, and analyze them in the same way as real HST images ("forward modeling"). This is a powerful tool for testing and comparing galaxy evolution models, since it allows us to make unbiased comparisons between the predicted and observed distributions of galaxy properties, while automatically taking into account all relevant selection effects. Our semi-empirical models populate each dark matter halo with a galaxy of determined stellar mass and scale radius. We compute the luminosity and spectrum of each simulated galaxy from its evolving stellar mass using stellar population synthesis models. We calculate the intrinsic scatter in the stellar mass-halo mass relation that naturally results from enforcing a monotonically increasing stellar mass along the merger history of each halo. The simulated galaxy images are drawn from cutouts of real galaxies from the Sloan Digital Sky Survey, with sizes and fluxes rescaled to match those of the model galaxies. The distributions of galaxy luminosities, sizes, and surface brightnesses depend on the adjustable parameters in the models, and they agree well with observations for reasonable values of those parameters. Measured galaxy magnitudes and sizes have significant magnitude-dependent biases, with both being underestimated near the magnitude detection limit. The fraction of galaxies detected and fraction of light detected also depend sensitively on the details of the model.