Type: Poster
Session: Posters (Monday & Tuesday)
Author: Joseph Santos

Abstract: With the Vera C. Rubin Observatory nearing commissioning, testing the analysis pipelines and determining how small our errors will be is important. To do this, the Dark Energy Science Collaboration Forecasting Topical Team runs mock data similar to forthcoming Rubin data through the analysis pipeline. In addition to our cosmological parameters, we must include many systematic effects in the analysis. If we do not allow these systematics to vary, our uncertainties on cosmology parameters will be underestimated. To get reasonable cosmological parameter constraints, we need to marginalize over these nuisance parameters. Allowing all cosmology and systematics parameters to vary means we must sample a high-dimensional space, which is time-consuming. We can reduce the computational time required by marginalizing analytically. We implement the analytical marginalization method described in Hadzhiyska et al. (2023) and broaden the nuisance parameters to include photometric redshifts and intrinsic alignment. We find that the analytic marginalization method significantly reduces computation time and obtains constraints consistent with traditional marginalization methods.