Roaster has a parameter optimization step prior to MCMC to attempt to reduce the burn-in period. But this optimization step often fails. We need a reliable likelihood optimizer in Roaster that works for a range of input galaxy images with both isolated and blended sources.
Roaster has a parameter optimization step prior to MCMC to attempt to reduce the burn-in period. But this optimization step often fails. We need a reliable likelihood optimizer in Roaster that works for a range of input galaxy images with both isolated and blended sources.