The next generation of large-scale structure surveys such as Euclid, DESI, LSST, SPHEREx, and SKA is going to provide an unprecedented amount of observational data. These surveys will map large portions of the sky, observing hundreds of millions of bright galaxies up to high redshifts, effectively transforming cosmology into a data-driven, precision science. It is more and more important to develop the right techniques to efficiently extract information from this data. The large number of ongoing efforts reach from new summary statistics for efficient data compression to full forward models for field level analysis and use an expanded range of statistical techniques. For any of these, blind mock data challenges are an important tool to demonstrate the unbiased inference of cosmological parameters, where complications can arise from uncertainties about non-linear galaxy formation as well as observational systematics. This is the second workshop with the aim to gather a broad range of proposed (new and established) methods for the data analysis of next-generation surveys. It will also offer a platform to discuss those methods in the light of ongoing data challenges and to plan future ones. The goal is to share expertise between groups and to identify common obstacles and solutions for establishing an end-to-end pipeline that can be applied to real data.
https://www.sexten-cfa.eu/event/new-strategies-for-extracting-cosmology-from-galaxy-surveys/\
Key participants
- Alice Pisani
- ChangHoon Hahn
- Martin Crocce
- Jiamin Hou
- Héctor Gil-Marin
- Pierre Zhang
