UC Irvine

One of the most compelling problems in physics today is understanding the nature of dark energy, a mysterious component driving the current accelerated cosmic expansion. The Dark Energy Spectroscopic Instrument (DESI) and the Rubin Observatory Legacy Survey of Space and Time (LSST) are Stage-IV Department of Energy (DOE) projects aimed at better understanding the nature of dark energy and its influence on the evolution of the universe. While DESI is a spectroscopic survey, and LSST provides multi-band photometry, their observations are complementary and can be combined to improve measurements of cosmological parameters.

One area of synergy lies in estimating the redshifts of extragalactic sources. The overlap between the DESI and LSST footprints is approximately 4,000 square degrees. While DESI will have a lower density of galaxies per square degree, having spectra for these targets will help to improve constraints on measured redshift distributions. The first part of the thesis will focus on developing and testing a state-of-the-art photometric redshift estimation algorithm on simulated LSST data. The algorithm employs a hierarchical Bayesian framework to simultaneously incorporate photometric, spectroscopic, and clustering information to constrain redshift probability distributions of populations of galaxies, as well as provide redshift estimates of their individual members. Once data from LSST arrives, this method can be tested and refined through training on real LSST targets whose counterparts lie within the DESI footprint. This will ultimately improve redshift estimates for other targets in LSST by providing spectroscopic prior information, and will be especially useful in the context of tomographic weak gravitational lensing, which derives a significant amount of uncertainty from imprecise redshift estimates.

Another crucial step in limiting weak lensing systematics involves understanding and mitigating image artifacts in the camera. This is important for identifying blended objects, as well as pinpointing biases in shear measurements. The third chapter of the thesis focuses on studying the systematics of the LSST instrument response by investigating anomalies in calibration sequences and developing testing software to analyze irregularities in bias frames. Making reliable, quantitative measurements that can be compared to requirements at the 1% level is necessary to avoid systematic biases in weak lensing shape measurements, which are often of the same order as the sensor distortions.

The second half of the thesis is devoted to developing software pipelines in preparation for the DESI survey. The fourth chapter discusses using a Gaussian mixture model (GMM) to characterize galaxy magnitudes and colors from DESI targeting data for the purpose of generating mock spectra. Results from the GMM are compared to density estimates for these features using extreme deconvolution, which simultaneously models the data and the noise to provide error-deconvolved distribution functions.

One of the final stages in the processing of mock spectra involves accounting for the noise contributions due to the atmosphere and the spectrograph response. The last chapter is devoted to reconfiguring a DESI software package that simulates this response to produce synthetic spectra for Lyman-alpha studies in the Extended Baryon Oscillation Survey (eBOSS). The original configuration is then used to validate the DESI sky model by comparing real sky brightnesses with simulated brightnesses generated under similar observing conditions.