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

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. 1 We use "halo" and "sub-halo" interchangeably, following Guo et al. (2010).

Is main-sequence galaxy star formation controlled by halo mass accretion?

“…Our primary motivation here is to understand whether halo MARs are responsible for the mass and redshift dependence of the SFR main sequence and its scatter. Similar models have been explored in the past for different purposes, including generating mock catalogs (Taghizadeh-Popp et al 2015) and understanding the different clustering of quenched and star-forming galaxies (Becker 2015). Using halo MARs, we operationally infer galaxy SFRs as follows.…”

Estimating Sky Level

“…We conclude that for the isolated galaxy case signal-to-noise ratio is a major factor in the surface brightness profile estimation. For example, see Taghizadeh-Popp et al (2015), who show an underestimate of galaxy size near the detection limit at multiple depths. This truncation in size is closely related to the bias in profile estimate.…”

Testing the Recovery of Intrinsic Galaxy Sizes and Masses of z ∼ 2 Massive Galaxies Using Cosmological Simulations

“…Mock observations of simulated galaxies are ideally suited to this task, as cosmological simulations now probe the complex star, gas, and dust geometry in the interstellar medium with high (sub-kiloparsec scale) resolution (e.g., Hopkins et al 2014;Schaye et al 2014;Vogelsberger et al 2014;Feldmann et al 2016). Recent studies have investigated the recovery of stellar masses (e.g., Wuyts et al 2009;Hayward & Smith 2015;Torrey et al 2015) and sizes (e.g., Wuyts et al 2010;Snyder et al 2015aSnyder et al , 2015bTaghizadeh-Popp et al 2015;Bottrell et al 2017) using mock observations. However, these studies have not simultaneously included dust, multiple viewing angles, high spatial resolution, observational point-spread functions (PSFs), and noise to test parameter recovery in high-redshift galaxies.…”