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  1. 1. white noise
  2. 2. ffp10 noise
  3. 3. ffp10 noise + residual masking
  4. 4. test no fg :
    1. 4.1 last results simus + tests
    2. 4.2 Results :
  5. 5. planck data
  6. 6. simulations
  7. 7. tests
    1. 7.1 Old results on Simulations (no more actual)
  8. 8. fsky=0.5
  9. 9. fsky=0.7
    1. 9.1 preliminary tests
  10. 10. masks :
  11. 11. white noise :
  12. 12. ffp10 noise :
  13. 13. beam cosine test :

1.  white noise

2.  ffp10 noise

3.  ffp10 noise + residual masking

Clfsky0.7_Rfsky0.6
Clfsky0.7_Rfsky0.7
Clfsky0.8_Rfsky0.6
Clfsky0.8_Rfsky0.7
Clfsky0.9_Rfsky0.5
Clfsky0.9_Rfsky0.6
Clfsky0.9_Rfsky0.7

4.  test no fg :

4.1  last results simus + tests

test + white noise
simus + white noise
test + ffp10 noise
test + ffp10 noise

4.2  Results :

5.  planck data

xVll cov

6.  simulations

end-to-end simulations

Clfsky = 0.5

white noise

xVll cov
MC cov

ffp10 noise

Clfsky = 0.8

white noise

ffp10 noise

7.  tests

Fixed fixed coeff alpha

with fg residuals

7.1  Old results on Simulations (no more actual)

8.  fsky=0.5

dust maskresidual mask

9.  fsky=0.7

dust maskresidual mask

9.1  preliminary tests

  • nside=16
  • cosine beam
    • l1 = 2ns-1
    • l2 = 3ns-1
  • fsky = 0.5

10.  masks :

  • dust mask : based on PySM dust template at 353. A cut is applied on the power of the signal
  • residuals mask : cut for which the pixel have the largest residuals power :

m = (1- \alpha_D - \alpha_S)^{-1} \cdot (\delta_D f_{353} + \delta_S f_{30} + \bar n_d + \alpha_D < n_D > + \alpha_S < n_S >

v = (1- \alpha_D - \alpha_S)^{-2} \cdot (\sigma^2 (n_D) + \alpha_D^2 \sigma^2 (n_D) + \alpha_S^2 \sigma^2 (n_S)

R = m / \sqrt(v / 300)

\sqrt{R_Q^2 + R_U^2} is the map of the power of the residuals. A cute is applied on the sky based on the amplitude of the pixel of this map.

dust maskresidual mask

11.  white noise :

noise :

no noise :

12.  ffp10 noise :

dust mask :

residual mask :

13.  beam cosine test :

l2 = 3*nside-1

l1 = 2l1 = 31