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(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.