From Galaxy Shapes to Cosmological Constraints: Forecasts for the LSST Dark Energy Science Collaboration

With the Vera C. Rubin Observatory's first light just around the corner, testing the analysis pipelines used to find cosmological constraints is essential. To do this, the Dark Energy Science Collaboration forecasting team runs mock data similar to future Vera C. Rubin Observatory data through the analysis pipeline. In addition to our cosmological parameters, we must include many systematic effects in the analysis. We must model various systematic effects to obtain realistic cosmology parameter constraints. To get reasonable cosmological parameter constraints, we need to marginalize over these nuisance parameters. Sampling all of the nuisance parameters that describe our systematic effects in order to marginalize over them requires sampling a high dimensional space, which is computationally intensive and time-consuming. We can reduce the number of parameters we need to sample and thus the computation time required by marginalizing over nuisance parameters analytically before sampling the cosmological parameters. We implement the analytical marginalization method described in Hadzhiyska et al. (2023) and find that we obtain constraints consistent with traditional marginalization methods that significantly cut computation time.