Speaker
Yuanyuan Zhang
(NSF NOIRLab)
Description
I will describe how OSG has enabled us to develop a simulation-based inference (SBI) method for cosmology analysis. For this application, we train an SBI model, based on a mixture density network (MDN), to derive posteriors for cosmological parameters from a data vector that describe observations of galaxy clusters in the universe. We use analytic models to generate mocks of the observational data vectors needed for building and testing the SBI method, and OSG enabled us to generate these large training and testing data sets.
Author
Yuanyuan Zhang
(NSF NOIRLab)