(David Alonso writing)
This is an updated version of the results shown in our previous post, where we looked at the case in Victor's forecasts using our map-based component separation code on PySM simulations. Main changes with respect to our previous iteration:
- Updated noise levels assuming Victor's levels are in polarization (instead of intensity).
- Assumed a delensing factor
- Introduced correlated noise. For this we considered noise curves made up of a flat component with the quoted levels and a power-law component that starts dominating at some scale . The power law index was determined from Victor's noise curves, and we explored (uncorrelated noise), and .
The final numbers agree qualitatively with Victor's forecast:
See our previous post for further details on the method.