In this dissertation, we investigate weak lensing cosmology and its astrophysical systematics by employing machine learning techniques. We focus on addressing the discrepancy between two previous weak lensing analyses on CFHTLenS data, understanding the impact of baryons on weak lensing statistics, and leveraging convolutional neural networks (CNNs) for constraining cosmological and baryonic parameters.
First, we perform a side-by-side comparison of the two-point correlation function and power spectrum analyses on CFHTLenS data, identifying excess power in the data on small scales and discussing potential origins of this excess power. Next, we study the effect of baryons on weak lensing statistics using the baryonic correction model, demonstrating that marginalizing over baryonic parameters will degrade constraints in the Ωm–σ8 parameter space, but the degradation can be mitigated by combining the lensing power spectrum and peak counts.
Second, we explore the use of CNNs to constrain cosmological and baryonic parameters. We find that CNNs can achieve tighter constraints in Ωm–σ8 space than traditional methods on simulation data. We then apply our pipeline to the HSC first-year weak lensing shear catalog. We find that statistical uncertainties of the parameters by the CNNs are smaller than those from the power spectrum and peak counts, showing that CNNs can extract additional cosmological information from weak lensing data even in a real experiment.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/0gqq-am89 |
| Date | January 2023 |
| Creators | Lu, Tianhuan |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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