We played a major role in developing techniques for the analysis of cosmic lensing data, and understanding its implications in the context of the wider cosmological framework.
Our work in this project spanned three main areas, corresponding to the key challenges in interpreting large scale imaging data to produce cosmological conclusions from cosmic lensing.
1. Extremely accurate measurement of galaxy shapes. We developed galaxy shape measurement methodologies and carried out large simulations to test and calibrate the results, providing one of the two galaxy shape catalogues used in the first year (Y1) cosmology results from the Dark Energy Survey (DES). We took a fresh look at shape measurement methodology and proposed a novel approach. We wrote the majority of the code used for the massive simulations used to calibrate the DES third year results (Y3), and contributed to tests of the quality of the results.
2. Measurement of galaxy clustering and shape two-point functions. Cross-correlating the positions and shapes of galaxies provides a powerful window onto the dark universe, but only if confounding effects are taken into account at sufficient accuracy. We led the galaxy clustering results from the first year of DES data, evaluating and removing the main potential contaminants. We showed how combination of the clustering and shape information could provide a key to help understanding the distribution of galaxies as a function of estimate distance from the observer (redshift). For the first time, we developed a way for uncertainties in galaxy redshifts to be incorporated into the standard cosmological parameter estimation framework (MCMC) - hyperrank, demonstrating that this would not be necessary for DES Y3 but paving the way for future years.
3. We contributed to the final cosmology constraints from DES Y1 and DES Y3 through our work in the above areas and by creating a fast pipeline which we used to evaluate the quality of the MCMC samplers and configurations, thereby influencing the choice of method, as well as developing and testing to the modified gravity constraint methodology. We used this work and our work on other key datasets including Planck, to put constraints on wider classes of cosmological model, and investigate tensions between different datasets.
Furthermore, we took a proactive approach to influencing the design of future experiments, including incorporating the potential of gravitational waves and standard sirens, and future data from the cosmic microwave background, taking an overview of future datasets and their potential to resolve tensions or discover new physics.
A major legacy of this project is the SkyPy open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software.
The results from the Dark Energy Survey have been widely disseminated, including via press releases. We have given a number of talks at seminars and conferences about the DES results and about the SkyPy project, as well as on our broader results on cosmological constraints.
