Search

Future astrophysical surveys will provide unprecedented datasets in both scale and precision for testing our most concordant theories of dark matter and dark energy. For the first time, the confidence of our understanding of the components of the Universe will be limited by systematic uncertainties rather than the statistical precision we can achieve. Improved modeling of measurement biases in survey data is necessary to achieve the theory-constraining power of ambitious future astrophysical surveys. Recent in-lab, on-sky, and simulated experiments offer opportunities to investigate coherence between systematic and phenomenological observables and invent new techniques to differentiate them. This thesis details my contribution to measuring the Dark Universe, and it is structured in two parts. The first part of this thesis improves template modeling of the foreground gamma-ray emission in the Galactic plane in 12 years of Fermi-LAT data. This has implications for the observed excess \gray{} emission near the Galactic center, which was previously associated with dark matter. We compare the likelihood of observing gamma-ray emission from cold hydrogen gas clouds using models derived from spectral line emission maps of abundant ($^{12}\mathrm{CO}$) and rare ($^{13}\mathrm{CO}$) tracer molecules. Using this approach, the rare tracer predicts previously unmodeled gamma-ray emission at $47\sigma$ significance, localized to the pixels around the densest regions, where emission is undercounted due to reabsorption along the line-of-sight. We also identify unassociated point-like sources in the Galactic plane which were previously smoothed out by re-absorption consistent with peaked gas structure as well as point-like gas structure which could have been mistaken for millisecond pulsars by earlier works. We further utilize a convolutional neural network (CNN) to try to predict the $^{13}\mathrm{CO}$ distribution in the same region, which could prove useful to predict the $^{13}\mathrm{CO}$ distribution in data-poor regions such as the Galactic center. The second part of this thesis develops a correction for the observed size-magnitude relation of stars, which are used calibrate observables for the Legacy Survey of Space and Time (LSST). The brighter-fatter effect results in distorted images of bright calibration stars, creating a flux-dependent point spread function (PSF) that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). Using data from the LSST camera, we compare the BFE in flat field and star images and test the ability of scalar correction methods to mitigate it. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs which were previously assumed to be negligible by a commonly used correction technique. We also find that poor assumptions of the correction result in unphysical violations of flux conservation in the images at the O(0.01\%) level. Based on these analyses, we then propose a new, unbiased correction methodology to account for these effects. With these improvements we can correct on average 95\% of the total effect in the sensors we tested. We use these results to predict the impact of these previously unmodeled effects on sensitive observables of inferred cosmological outcomes and advise data processing strategies to limit the impact of the effects on LSST science pathways.

The Legacy Survey of Space and Time (LSST) carried out by the Vera C. Rubin Observatory is one of the most ambitious optical surveys planned in the near future. It will generate $\sim 20$~TB of data every night during its 10 year survey duration. Simulated data sets (``Data Challenges'') have been generated by the LSST Dark Energy Science Collaboration (DESC) to allow development of efficient analysis algorithms, evaluation of their performance and scaling capabilities, and to study the impact of systematic uncertainties. In this work we focus on the analysis of Large Scale Structure using galaxies selected from the DESC Data Challenge 2 (DC2) corresponding to a survey area of 300 square degrees. We develop and perform an end-to-end analysis of the galaxy Large Scale Structure with the DC2 data, validating the extraction of galaxy power spectra and galaxy bias in the presence of complex systematics. We provide a range of strong consistency checks of both the simulated data and the analysis techniques.Based on matching selected galaxies with the DC2 truth catalog, we optimize selection to obtain a clean sample of galaxies. After generating and applying survey masks, we produce galaxy density maps binned in redshift, with six bins from $z = 0.2 - 1.4$. We generate the power spectra measurements from the processed data and fit the data using theoretical predictions to estimate the galaxy bias as a function of redshift. Finally, we study the effect of systematic uncertainties on estimation of the galaxy bias.We find that the relative uncertainty $\sigma_\ell / C_\ell$ in the measured angular power spectra is smaller than 10\% in most cases, and smaller than 4\% at multipoles above 1500.%Changes in the angular power spectra due to systematic uncertainties are found to be on average of order 0.5\%, i.e. $\Delta C_\ell / C_\ell \approx 0.5\%$, with a maximum deviation of 1.5\%.%Systematic effects are small compared to the statistical uncertainty of the measured angular power spectra: $\Delta C_\ell \sim 0.1 \sigma_\ell$ on average with a maximum of $0.25 \sigma_\ell$.