Speaker
Description
We study the robustness of a simulation-based inference (SBI) method in the context of cosmological parameter estimation from galaxy cluster abundance in mock cluster datasets. I will describe an application where we train an SBI model, based on a mixture density network (MDN), to derive posteriors for cosmological parameters from a stacked cluster data vector constructed using an analytic model for the galaxy cluster halo mass function. We compare the SBI posteriors to posteriors from an equivalent MCMC analysis that uses the same analytic form for the likelihood. Although this idealized analysis is designed for optical surveys, we have learned that the SBI method can be an effective method for galaxy cluster cosmology analysis and their results are highly consistent with those derived from the MCMC method. I will describe the results from the SBI and MCMC analyses and the lessons learned from the comparison.
| Would you be interested in presenting a poster if the conference is oversubcribed? | Yes |
|---|
