1. Probing the Dark Universe at the Pixel-Level in Large-Scale Surveys
- Author
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Broughton, Alexander Morris, Murgia, Simona1, Broughton, Alexander Morris, Broughton, Alexander Morris, Murgia, Simona1, and Broughton, Alexander Morris
- Abstract
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}
- Published
- 2024