MapBasedRb

From CMB-S4 wiki
Jump to navigationJump to search

(David Alonso writing)

Summary

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.