Difference between revisions of "Characterization of simulations for configurations 30-33"

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== Noise characterization ==
 
== Noise characterization ==
  
To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the noise spectra obtained from the simulations with the expectation based on the input noise model.   
+
To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the average of the noise spectra obtained from the simulations with the expectation based on the input noise model.   
  
In addition to the expected average, we can also predict the noise variance. For inverse noise variance weighted maps, one finds
+
In addition to the expected average, we can also predict the scatter we expect analytically. For each configuration, we show a plot comparing the predicted standard deviation for the noise spectra with that obtained from the simulations.  
\sigma(N_\ell)=\sqrt{\frac{2}{(2\ell+1)\delta\ell f_{\rm sky}^{noise}}N_\ell. The comparison of the predicted standard deviation for the noise spectra with that obtained from the simulations is also shown for each configuration.  
 
  
 
Finally, we show sample realizations of the noise maps for the Stokes Q parameter for the different frequency bands.
 
Finally, we show sample realizations of the noise maps for the Stokes Q parameter for the different frequency bands.
  
The theoretical expectation and simulations typically agree to within a few per cent. The lowest bin is an exception and (just like in the data challenge simulations) the noise estimated from the simulations exceeds the theory curve by as much as 20 per cent. This is caused by the prescription used (here and in the community more generally) to generate the apodized noise maps, which assumes that reweighting pixels by Nobs leaves the power spectrum unchanged. This assumption fails as on scales that approach scales on which the hits map varies. This can in principle be corrected but has not been done here.  
+
The theoretical expectation and simulations typically agree to within a few per cent. The lowest bin is an exception and (just like in the data challenge simulations) the noise estimated from the simulations exceeds the theory curve by as much as 20 per cent. This is caused by the prescription used here (and in the community more generally) to generate the apodized noise maps, which assumes that reweighting pixels by Nobs leaves the power spectrum unchanged. This assumption fails as on scales that approach scales on which the hits map varies. This could be corrected but has not been done here.  
  
  
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[[File:l2sigmaNl_33.png|600 px]]
 
[[File:l2sigmaNl_33.png|600 px]]
 
 
  
 
== Sample noise realizations ==
 
== Sample noise realizations ==

Revision as of 23:59, 30 March 2019

Noise characterization

To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the average of the noise spectra obtained from the simulations with the expectation based on the input noise model.

In addition to the expected average, we can also predict the scatter we expect analytically. For each configuration, we show a plot comparing the predicted standard deviation for the noise spectra with that obtained from the simulations.

Finally, we show sample realizations of the noise maps for the Stokes Q parameter for the different frequency bands.

The theoretical expectation and simulations typically agree to within a few per cent. The lowest bin is an exception and (just like in the data challenge simulations) the noise estimated from the simulations exceeds the theory curve by as much as 20 per cent. This is caused by the prescription used here (and in the community more generally) to generate the apodized noise maps, which assumes that reweighting pixels by Nobs leaves the power spectrum unchanged. This assumption fails as on scales that approach scales on which the hits map varies. This could be corrected but has not been done here.


Configuration 30

Comparison of simulation average with noise model

L2Nl 30.png

Comparison of noise variance with expectation based on noise model and weight map

L2sigmaNl 30.png


Configuration 31

Comparison of simulation average with noise model

L2Nl 31.png

Comparison of noise variance with expectation based on noise model and weight map

L2sigmaNl 31.png


Configuration 32

Comparison of simulation average with noise model

L2Nl 32.png

Comparison of noise variance with expectation based on noise model and weight map

L2sigmaNl 32.png


Configuration 33

Comparison of simulation average with noise model

L2Nl 33.png

Comparison of noise variance with expectation based on noise model and weight map

L2sigmaNl 33.png

Sample noise realizations

Configuration 30

Noise realization 0001

30.0001.png

Noise realization 0002

30.0002.png

Configuration 31

Noise realization 0001

31.0001.png

Noise realization 0002

31.0002.png

Configuration 32

Noise realization 0001

32.0001.png

Noise realization 0002

32.0002.png

Configuration 33

Noise realization 0001

33.0001.png

Noise realization 0002

33.0002.png