Build a deep learning based emulator of cosmological parameter estimation MCMC to support new types of LSS summary statistics (such as lensing mass maps).
Demonstrate that we can obtain comparable posteriors in Omega_m and sigma_8 using both MCMC and the new BCVAE pipeline given simulated tomographic gal-gal or shear-shear two-point functions.
Resources and skills needed
We have an initial BCVAE implementation to get started and are working to compare with existing examples in the Cobaya code.
Skills needed:
Cosmological parameter estimation with Cobaya and/or Cosmosis
Eventually, we aim to build and demonstrate cosmological parameter inference pipelines for non-traditional summary statistics of large-scale structure that may contain more information than the 3x2 (galaxy and shear) two-point functions. We have previously proposed to use weak lensing mass maps directly in a Bayesian forward-model to constrain cosmological parameters by simulating full LSS light-cones within a Markov Chain Monte Carlo analysis. Such an approach can quickly become computationally intractable (although there are some successes in the literature). We aim to explore recently published advances in auto-encoders to use deep learning to produce accurate multivariate posterior distributions.
Bayesian Variational Autoencoder cosmological parameter inference pipeline
Build a deep learning based emulator of cosmological parameter estimation MCMC to support new types of LSS summary statistics (such as lensing mass maps).
Contacts: Michael Schneider, Ryan Dana, Bob Armstrong @rearmstr Day/Time: Tues-Fri PST sprint hours Main communication channel: desc-wl-pipelines GitHub repo: https://github.com/mdschneider/cosmo_bcvae Pitch slides: https://confluence.slac.stanford.edu/download/attachments/279347747/DESC_sprint_BCVAE_20201201.pdf?api=v2
Goals and deliverable
Demonstrate that we can obtain comparable posteriors in Omega_m and sigma_8 using both MCMC and the new BCVAE pipeline given simulated tomographic gal-gal or shear-shear two-point functions.
Resources and skills needed
We have an initial BCVAE implementation to get started and are working to compare with existing examples in the Cobaya code.
Skills needed:
Detailed description
Eventually, we aim to build and demonstrate cosmological parameter inference pipelines for non-traditional summary statistics of large-scale structure that may contain more information than the 3x2 (galaxy and shear) two-point functions. We have previously proposed to use weak lensing mass maps directly in a Bayesian forward-model to constrain cosmological parameters by simulating full LSS light-cones within a Markov Chain Monte Carlo analysis. Such an approach can quickly become computationally intractable (although there are some successes in the literature). We aim to explore recently published advances in auto-encoders to use deep learning to produce accurate multivariate posterior distributions.