Characterizing the impact of photometric redshift uncertainties on LSST dark energy constraints using analytic nuisance parameter marginalization

Galaxy clustering and weak gravitational lensing data can be used to measure a range of cosmological parameters describing the universe's contents and evolution. To infer these cosmological parameters using photometric large-scale structure data, observational and astrophysical sources of uncertainty must be taken into account. In practice, this means that many nuisance parameters must be included in the analysis, alongside a smaller number of cosmological parameters. Sampling all these nuisance parameters with traditional rejection sampling methods is computationally intensive and time consuming. One way to obtain the cosmological parameter constraints more efficiently is to use analytical approximation methods to efficiently marginalize over these nuisance parameters. We use analytic marginalization to study the effects of redshift uncertainties on dark energy constraints from LSST, and compare the results with those obtained by directly sampling the nuisance parameters.