We eventually want to scale up the spectral inference to more sources with supercomputing resources. For now, let's focus on a few good demo sources, with good priors, good initial guesses, good noise models, and reproducible workflows.
Tasks:
[ ] Make sure all order have the same user-defined priors
[ ] Spot check the residuals for all initial guesses, iteratively refine
[ ] git commit all config.yaml and s0_phi.json files
[ ] Run token MCMC of ~3 samples to make sure everything compiles
[ ] pull all changes to NAS HECC resources
[ ] git commit the conda environment.yml file and .envrc file for reproducibility
[ ] Pick system for deployment-- pleiades or other
We eventually want to scale up the spectral inference to more sources with supercomputing resources. For now, let's focus on a few good demo sources, with good priors, good initial guesses, good noise models, and reproducible workflows.
Tasks: