Towards High-redshift Cosmology with Lyman-break Galaxies Detected by LSST

Abstract

The Vera C. Rubin Observatory is set to begin the Legacy Survey of Space and Time (LSST), a generation-defining astronomical survey that will image the entire southern sky in 6 photometric bands to unprecedented depth.LSST promises to discover hundreds-of-millions of high-redshift galaxies, opening a huge, previously unprobed volume of the universe to precision cosmology. These high-redshift constraints will provide new ways to test the standard cosmological model and have the potential to shed new light on the many tensions present in modern cosmology, including the evolution of dark energy, the sum of neutrino masses, and the mass density of the cosmos. Extracting information about the evolution of the high-redshift universe from LSST data will require careful modeling and new analysis tools to control systematic errors. This dissertation develops new methods for estimating the distance to galaxies using photometric data (photometric redshifts, or photo-z's) and studying the systematic errors that plague them.Using machine learning tools, we show that galaxy spectral templates can be learned directly from broadband photometry, increasing the accuracy and precision of template-based photo-z estimation, which will figure prominently in the analysis of high-redshift galaxies. Using normalizing flows, we develop a statistical forward model of photometric galaxy catalogs, enabling new and more reliable studies of photo-z calibration, including consistent evaluation of photo-z posterior distributions. We then discuss optimizing the LSST survey strategy for the detection and photo-z estimation of high-redshift galaxies, before forecasting number densities by combining simulations of LSST with calibration data from precursor surveys.Using this model, we forecast the power of LSST for constraining the growth of large scale structure and the evolution of dark energy, finding that a joint analysis of high- and low-redshift data increases the constraining power of LSST by a factor of three compared to constraints from low-redshift data alone. We also study various sources of systematic error, quantifying their impact on cosmological constraints, and considering how data from LSST and CMB lensing surveys can be combined to reduce the impact of these errors. A series of appendices present research on wave-front estimation for the Rubin Observatory's active optics system (AOS), which maintains the telescope's optical alignment and mirror figure to correct for optical aberrations and deliver high image quality across Rubin's wide field of view.First we describe the physical components of the AOS, before deriving an algorithm for wave-front estimation in Rubin's fast, wide-field optical system. We then introduce and validate a deep learning algorithm for wave-front estimation, showing it to be faster and more robust than traditional methods. We conclude by studying the information content carried by the shape and intensity of stars in the defocused images used for wave-front estimation.