Organized by David Weinberg (Astronomy), Jeremy Tinker (NYU), Ben Wibking (Astronomy)
Ongoing and future large-scale galaxy surveys will yield unparalleled maps of universe. However, for these maps, the most precise measurements of galaxy spatial clustering are at non-linear scales. These scales are largely ignored due to the complexities of modeling non-linearities in structure formation and in galaxy bias. The amount of cosmological constraining power being left on the table is currently unknown because the tools to address this question have not existed. New advances in computing and data science techniques are beginning to make such goals achievable.
The purpose of this workshop is to bring together different groups working on this problem. These groups focus on different aspects of cosmological inference from non-linear clustering, including data analysis of imaging surveys, analysis of redshift surveys, or producing tools to model the non-linear distribution of dark matter. During the workshop, the groups will present their current status, as well as current challenges. Discussions will focus on both overcoming these challenges, and on how to maximize the power of non-linear galaxy clustering data.