Some Monte-Carlo Maps

date:22/06/09

CONCLUSION:

Caracteristics of the data:

Data simulated @ CC

group/runnamesimupapier6
bolo143-5
nSide512
IMO'1127593'
signal name'cmb_gal_dipcosmo_gauss'
first ring580
last ring13080

Maps:

  • Signal alone:
Signal alone (from Polmap without Offsets)Signal alone (from Polmap with Offsets)Stripes due to the Signal
  • With NOISE:
    • WHITE NOISE
Signal+Noise (from Polmap without Offsets)Signal+Noise (from Polmap with Offsets)
WhiteNoise and StripesWhite NoiseStripes
  • Fknee = 1e-4
Signal+Noise (from Polmap without Offsets)Signal+Noise (from Polmap with Offsets)
WhiteNoise and StripesWhite NoiseStripes
  • Fknee = 1e-3
Signal+Noise (from Polmap without Offsets)Signal+Noise (from Polmap with Offsets)
WhiteNoise and StripesWhite NoiseStripes
  • Fknee = 1e-2
Signal+Noise (from Polmap without Offsets)Signal+Noise (from Polmap with Offsets)
WhiteNoise and StripesWhite NoiseStripes
  • Fknee = 1e-1
Signal+Noise (from Polmap without Offsets)Signal+Noise (from Polmap with Offsets)
WhiteNoise and StripesWhite NoiseStripes
 - When fknee increases, the stripes are more visible! And even dominates over the dipole for fknee=1e-1.
  • Maps of hit-weight

Histograms of Maps:

Hereafter is displayed the histograms of Maps un-weighted by the LevelS Map of Hit!!

Noise typeWN1e-41e-31e-21e-1
histo of WN part
histo of Strip part
histo of Noise (WN+Strips) part
residu of WN
residu of Strip
residu of Noise 
 - One can see on the residu of Noise that when fknee increase the residu tends to have the shape of the strips!

*Results of Fit:

WN correspond to the WN part of the Noise
Oo correspond to the Strips part of the Noise
WO correspond to the Total Noise

Expected sigma_sample: 62e-6*sqrt(172.18)=0.000813548

 WN1e-41e-31e-21e-1
 
reduced chisq_WN1.00101050.981199811.01881381.14582560.98739553
sigma_WN_array0.000812898610.000812916480.000812845610.000813381210.00086715036
err_sigma_WN_array3.2361700e-073.2366148e-073.2341700e-073.2360148e-073.4593197e-07
diff_sigma_WN_array-6.4908961e-07-6.3121204e-07-7.0208927e-07-1.6648832e-075.3602661e-05
nb_of_sig_WN_array-2.0057340-1.9502229-2.1708484-0.51448565154.95145
 
chisq_Oo_array288.8221384.68720522.39564919.20275718.696559
sigma_Oo_array7.1422004e-050.000110245810.000833854000.00832720650.083294972
err_sigma_Oo_array3.3561662e-084.7615892e-083.4892290e-073.4783295e-063.4785920e-05
diff_sigma_Oo_array-0.00074212569-0.000703301892.0306305e-050.00751365880.082481425
nb_of_sig_Oo_array-22112.305-14770.31958.1971122160.13432371.1152
 
chisq_WO_array1.10654560.992467503.876299218.78843518.776515
sigma_WO_array0.000816782130.000821110870.00117114990.00836953480.083298992
err_sigma_WO_array3.2489180e-073.2679426e-074.7542059e-073.4932257e-063.4783516e-05
diff_sigma_WO_array3.2344330e-067.5631743e-060.000357602200.00755598710.082485444
nb_of_sig_WO_array9.955416023.143534752.180712163.04012371.3947
 - For the WN part of the Noise, the results of the fit is "relatively" close to the expected value

but it is always at 2-sigma when compared to the precision of the estimation of sigma.