Faint Masking Results
Figure 1. - Perseus likelihoods.
Figure 2. - California likelihoods.
Figure 3. - Taurus likelihoods.
Figure 4. - perseus masks.
We’re testing convergence of the DGR during masking outlined in the last post.
Figure 1. - The left column represents the initial masking performed with a width of km/s to create the initial map used in masking, the right, an HI width of km/s.
Perhaps a more robust way to derive the mask would be to require that the DGR is nearly the same over several iterations, not just one subsequent iteration. However this process is working, and perhaps we should just leave it alone.
The likelihood calculation now includes multiprocessing framework. This speeds
up the calculation by around 50% per additional CPU. See this
cloudpy. Bip has 12 CPUs, so the speed increase is notable.