Are the ultra-high-redshift galaxies at z > 10 surprising in the context of standard galaxy formation models?

A substantial number of ultra-high redshift (8 < z < 17) galaxy candidates have been detected with JWST, posing the question: are these observational results surprising in the context of current galaxy formation models? We address this question using the well-established Santa Cruz semi-analytic models, implemented within merger trees from the new suite of cosmological N-body simulations GUREFT, which were carefully designed for ultra-high redshift studies. Using our fiducial models calibrated at z=0, we present predictions for stellar mass functions, rest-frame UV luminosity functions, and various scaling relations. We find that our (dust-free) models predict galaxy number densities at z~11 (z~ 13) that are an order of magnitude (a factor of ~30) lower than the observational estimates. We estimate the uncertainty in the observed number densities due to cosmic variance, and find that it leads to a fractional error of 30-70% at z=11 (25-150% at z=13) for the available observed fields. We explore which processes in our models are most likely to be rate-limiting for the formation of luminous galaxies at these early epochs, considering the halo formation rate, gas cooling, star formation, and stellar feedback, and conclude that it is mainly efficient stellar-driven winds. We find that a modest boost of a factor of ~4 to the UV luminosities, which could arise from a top-heavy stellar initial mass function characteristic of Pop III stars, would bring our current models into agreement with the observations.Comment: 20 pages, 15 figures, submitted to MNRA

Mock Galaxy Surveys for HST and JWST from the IllustrisTNG Simulations

We present and analyze a series of synthetic galaxy survey fields based on the IllustrisTNG Simulation suite. With the Illustris public data release and JupyterLab service, we generated a set of twelve lightcone catalogs covering areas from 5 to 365 square arcminutes, similar to several JWST Cycle 1 programs, including JADES, CEERS, PRIMER, and NGDEEP. From these catalogs, we queried the public API to generate simple mock images in a series of broadband filters used by JWST-NIRCam and the Hubble Space Telescope cameras. This procedure generates wide-area simulated mosaic images that can support investigating the predicted evolution of galaxies alongside real data. Using these mocks, we demonstrate a few simple science cases, including morphological evolution and close pair selection. We publicly release the catalogs and mock images through MAST, along with the code used to generate these projects, so that the astrophysics community can make use of these products in their scientific analyses of JWST deep field observations.Comment: Accepted to MNRA

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures

Semi-analytic forecasts for JWST -- II. physical properties and scaling relations for galaxies at z = 4-10

The long-anticipated James Webb Space Telescope (JWST) will be able to directly detect large samples of galaxies at very high redshift. Using the well-established, computationally efficient Santa Cruz semi-analytic model, with recently implemented multiphase gas partitioning and H2-based star formation recipes, we make predictions for a wide variety of galaxy properties for galaxy populations at $z$ = 4-10. In this work, we provide forecasts for the physical properties of high-redshift galaxies and links to their photometric properties. With physical parameters calibrated only to $z\sim0$ observations, our model predictions are in good agreement with current observational constraints on stellar mass and star formation rate distribution functions up to $z \sim 8$. We also provide predictions representing wide, deep, and lensed JWST survey configurations. We study the redshift evolution of key galaxy properties and the scaling relations among them. Taking advantage of our models' high computational efficiency, we study the impact of systematically varying the model parameters. All distribution functions and scaling relations presented in this work are available at https://www.simonsfoundation.org/semi-analytic-forecasts-for-jwst/.Comment: 28 pages, 22 figures, Accepted for publication in MNRA